diff --git a/.gitignore b/.gitignore index 7ebd58294..326b85948 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ tool_call_benchmark.py run_maibot_core.bat run_napcat_adapter.bat run_ad.bat +s4u.s4u llm_tool_benchmark_results.json MaiBot-Napcat-Adapter-main MaiBot-Napcat-Adapter diff --git a/Dockerfile b/Dockerfile index 23165a23e..be76277c3 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.13.2-slim-bookworm +FROM python:3.13.5-slim-bookworm COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/ # 工作目录 diff --git a/README.md b/README.md index 69c36e595..f450fc0a4 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,9 @@ ## 🔥 更新和安装 -**最新版本: v0.8.0** ([更新日志](changelogs/changelog.md)) + +**最新版本: v0.8.1** ([更新日志](changelogs/changelog.md)) + 可前往 [Release](https://github.com/MaiM-with-u/MaiBot/releases/) 页面下载最新版本 可前往 [启动器发布页面](https://github.com/MaiM-with-u/mailauncher/releases/tag/v0.1.0)下载最新启动器 **GitHub 分支说明：** @@ -53,7 +55,7 @@ - `classical`: 旧版本(停止维护) ### 最新版本部署教程 -- [从0.6升级须知](https://docs.mai-mai.org/faq/maibot/update_to_07.html) +- [从0.6/0.7升级须知](https://docs.mai-mai.org/faq/maibot/update_to_07.html) - [🚀 最新版本部署教程](https://docs.mai-mai.org/manual/deployment/mmc_deploy_windows.html) - 基于 MaiCore 的新版本部署方式(与旧版本不兼容) > [!WARNING] @@ -67,10 +69,10 @@ ## 💬 讨论 - [四群](https://qm.qq.com/q/wGePTl1UyY) | - [一群](https://qm.qq.com/q/VQ3XZrWgMs)(已满) | - [二群](https://qm.qq.com/q/RzmCiRtHEW)(已满) | - [五群](https://qm.qq.com/q/JxvHZnxyec)(已满) | - [三群](https://qm.qq.com/q/wlH5eT8OmQ)(已满) + [一群](https://qm.qq.com/q/VQ3XZrWgMs) | + [二群](https://qm.qq.com/q/RzmCiRtHEW) | + [五群](https://qm.qq.com/q/JxvHZnxyec) | + [三群](https://qm.qq.com/q/wlH5eT8OmQ) ## 📚 文档 diff --git a/bot.py b/bot.py index 16c264cbb..a3e49fceb 100644 --- a/bot.py +++ b/bot.py @@ -314,10 +314,17 @@ if __name__ == "__main__": # Schedule tasks returns a future that runs forever. # We can run console_input_loop concurrently. main_tasks = loop.create_task(main_system.schedule_tasks()) - console_task = loop.create_task(console_input_loop(main_system)) - # Wait for all tasks to complete (which they won't, normally) - loop.run_until_complete(asyncio.gather(main_tasks, console_task)) + # 仅在 TTY 中启用 console_input_loop + if sys.stdin.isatty(): + logger.info("检测到终端环境，启用控制台输入循环") + console_task = loop.create_task(console_input_loop(main_system)) + # Wait for all tasks to complete (which they won't, normally) + loop.run_until_complete(asyncio.gather(main_tasks, console_task)) + else: + logger.info("非终端环境，跳过控制台输入循环") + # Wait for all tasks to complete (which they won't, normally) + loop.run_until_complete(main_tasks) except KeyboardInterrupt: # loop.run_until_complete(get_global_api().stop()) diff --git a/changelogs/changelog.md b/changelogs/changelog.md index 2c81f150e..bef8ab146 100644 --- a/changelogs/changelog.md +++ b/changelogs/changelog.md @@ -1,5 +1,29 @@ # Changelog +## [0.8.1] - 2025-6-27 + +功能更新: + +- normal现在和focus一样支持tool +- focus现在和normal一样每次调用lpmm +- 移除人格表达 + +优化和修复： + +- 修复表情包配置无效问题 +- 合并normal和focus的prompt构建 +- 非TTY环境禁用console_input_loop +- 修复过滤消息仍被存储至数据库的问题 +- 私聊强制开启focus模式 +- 支持解析reply_to和at +- 修复focus冷却时间导致的固定沉默 +- 移除豆包画图插件，此插件现在插件广场提供 +- 修复表达器无法读取原始文本 +- 修复normal planner没有超时退出问题 + + + + ## [0.8.0] - 2025-6-27 MaiBot 0.8.0 现已推出！ diff --git a/docker-compose.yml b/docker-compose.yml index 9bd7172c6..b2ce0a31e 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,22 +1,29 @@ services: adapters: container_name: maim-bot-adapters + #### prod #### image: unclas/maimbot-adapter:latest # image: infinitycat/maimbot-adapter:latest + #### dev #### + # image: unclas/maimbot-adapter:dev + # image: infinitycat/maimbot-adapter:dev environment: - TZ=Asia/Shanghai # ports: # - "8095:8095" volumes: - - ./docker-config/adapters/config.toml:/adapters/config.toml + - ./docker-config/adapters/config.toml:/adapters/config.toml # 持久化adapters配置文件 + - ./data/adapters:/adapters/data # adapters 数据持久化 restart: always networks: - maim_bot + core: container_name: maim-bot-core + #### prod #### image: sengokucola/maibot:latest # image: infinitycat/maibot:latest - # dev + #### dev #### # image: sengokucola/maibot:dev # image: infinitycat/maibot:dev environment: @@ -25,15 +32,15 @@ services: # - PRIVACY_AGREE=42dddb3cbe2b784b45a2781407b298a1 # 同意EULA # ports: # - "8000:8000" -# - "27017:27017" volumes: - ./docker-config/mmc/.env:/MaiMBot/.env # 持久化env配置文件 - ./docker-config/mmc:/MaiMBot/config # 持久化bot配置文件 - ./data/MaiMBot/maibot_statistics.html:/MaiMBot/maibot_statistics.html #统计数据输出 - - ./data/MaiMBot:/MaiMBot/data # NapCat 和 NoneBot 共享此卷，否则发送图片会有问题 + - ./data/MaiMBot:/MaiMBot/data # 共享目录 restart: always networks: - maim_bot + napcat: environment: - NAPCAT_UID=1000 @@ -43,13 +50,14 @@ services: - "6099:6099" volumes: - ./docker-config/napcat:/app/napcat/config # 持久化napcat配置文件 - - ./data/qq:/app/.config/QQ # 持久化QQ本体并同步qq表情和图片到adapters - - ./data/MaiMBot:/MaiMBot/data # NapCat 和 NoneBot 共享此卷，否则发送图片会有问题 + - ./data/qq:/app/.config/QQ # 持久化QQ本体 + - ./data/MaiMBot:/MaiMBot/data # 共享目录 container_name: maim-bot-napcat restart: always image: mlikiowa/napcat-docker:latest networks: - maim_bot + sqlite-web: image: coleifer/sqlite-web container_name: sqlite-web @@ -62,6 +70,7 @@ services: - SQLITE_DATABASE=MaiMBot/MaiBot.db # 你的数据库文件 networks: - maim_bot + networks: maim_bot: driver: bridge diff --git a/s4u.s4u1 b/s4u.s4u1 new file mode 100644 index 000000000..e69de29bb diff --git a/src/api/main.py b/src/api/main.py index 81cd5a24a..598b8aec5 100644 --- a/src/api/main.py +++ b/src/api/main.py @@ -109,3 +109,4 @@ async def get_system_basic_info(): def start_api_server(): """启动API服务器""" get_global_server().register_router(router, prefix="/api/v1") + # pass diff --git a/src/audio/mock_audio.py b/src/audio/mock_audio.py new file mode 100644 index 000000000..9772fdad9 --- /dev/null +++ b/src/audio/mock_audio.py @@ -0,0 +1,62 @@ +import asyncio +from src.common.logger import get_logger + +logger = get_logger("MockAudio") + + +class MockAudioPlayer: + """ + 一个模拟的音频播放器，它会根据音频数据的"长度"来模拟播放时间。 + """ + + def __init__(self, audio_data: bytes): + self._audio_data = audio_data + # 模拟音频时长：假设每 1024 字节代表 0.5 秒的音频 + self._duration = (len(audio_data) / 1024.0) * 0.5 + + async def play(self): + """模拟播放音频。该过程可以被中断。""" + if self._duration <= 0: + return + logger.info(f"开始播放模拟音频，预计时长: {self._duration:.2f} 秒...") + try: + await asyncio.sleep(self._duration) + logger.info("模拟音频播放完毕。") + except asyncio.CancelledError: + logger.info("音频播放被中断。") + raise # 重新抛出异常，以便上层逻辑可以捕获它 + + +class MockAudioGenerator: + """ + 一个模拟的文本到语音（TTS）生成器。 + """ + + def __init__(self): + # 模拟生成速度：每秒生成的字符数 + self.chars_per_second = 25.0 + + async def generate(self, text: str) -> bytes: + """ + 模拟从文本生成音频数据。该过程可以被中断。 + + Args: + text: 需要转换为音频的文本。 + + Returns: + 模拟的音频数据（bytes）。 + """ + if not text: + return b"" + + generation_time = len(text) / self.chars_per_second + logger.info(f"模拟生成音频... 文本长度: {len(text)}, 预计耗时: {generation_time:.2f} 秒...") + try: + await asyncio.sleep(generation_time) + # 生成虚拟的音频数据，其长度与文本长度成正比 + mock_audio_data = b"\x01\x02\x03" * (len(text) * 40) + logger.info(f"模拟音频生成完毕，数据大小: {len(mock_audio_data) / 1024:.2f} KB。") + return mock_audio_data + except asyncio.CancelledError: + logger.info("音频生成被中断。") + raise # 重新抛出异常 diff --git a/src/chat/express/expression_selector.py b/src/chat/express/expression_selector.py index ca63db943..b85f53b79 100644 --- a/src/chat/express/expression_selector.py +++ b/src/chat/express/expression_selector.py @@ -80,14 +80,16 @@ class ExpressionSelector: ) def get_random_expressions( - self, chat_id: str, style_num: int, grammar_num: int, personality_num: int + self, chat_id: str, total_num: int, style_percentage: float, grammar_percentage: float ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]: ( learnt_style_expressions, learnt_grammar_expressions, - personality_expressions, ) = self.expression_learner.get_expression_by_chat_id(chat_id) + style_num = int(total_num * style_percentage) + grammar_num = int(total_num * grammar_percentage) + # 按权重抽样（使用count作为权重） if learnt_style_expressions: style_weights = [expr.get("count", 1) for expr in learnt_style_expressions] @@ -101,13 +103,7 @@ class ExpressionSelector: else: selected_grammar = [] - if personality_expressions: - personality_weights = [expr.get("count", 1) for expr in personality_expressions] - selected_personality = weighted_sample(personality_expressions, personality_weights, personality_num) - else: - selected_personality = [] - - return selected_style, selected_grammar, selected_personality + return selected_style, selected_grammar def update_expressions_count_batch(self, expressions_to_update: List[Dict[str, str]], increment: float = 0.1): """对一批表达方式更新count值，按文件分组后一次性写入""" @@ -174,7 +170,7 @@ class ExpressionSelector: """使用LLM选择适合的表达方式""" # 1. 获取35个随机表达方式（现在按权重抽取） - style_exprs, grammar_exprs, personality_exprs = self.get_random_expressions(chat_id, 25, 25, 10) + style_exprs, grammar_exprs = self.get_random_expressions(chat_id, 50, 0.5, 0.5) # 2. 构建所有表达方式的索引和情境列表 all_expressions = [] @@ -196,14 +192,6 @@ class ExpressionSelector: all_expressions.append(expr_with_type) all_situations.append(f"{len(all_expressions)}.{expr['situation']}") - # 添加personality表达方式 - for expr in personality_exprs: - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - expr_with_type = expr.copy() - expr_with_type["type"] = "style_personality" - all_expressions.append(expr_with_type) - all_situations.append(f"{len(all_expressions)}.{expr['situation']}") - if not all_expressions: logger.warning("没有找到可用的表达方式") return [] @@ -260,7 +248,7 @@ class ExpressionSelector: # 对选中的所有表达方式，一次性更新count数 if valid_expressions: - self.update_expressions_count_batch(valid_expressions, 0.003) + self.update_expressions_count_batch(valid_expressions, 0.006) # logger.info(f"LLM从{len(all_expressions)}个情境中选择了{len(valid_expressions)}个") return valid_expressions diff --git a/src/chat/express/exprssion_learner.py b/src/chat/express/exprssion_learner.py index a18961ef1..9fcb69687 100644 --- a/src/chat/express/exprssion_learner.py +++ b/src/chat/express/exprssion_learner.py @@ -74,16 +74,13 @@ class ExpressionLearner: ) self.llm_model = None - def get_expression_by_chat_id( - self, chat_id: str - ) -> Tuple[List[Dict[str, str]], List[Dict[str, str]], List[Dict[str, str]]]: + def get_expression_by_chat_id(self, chat_id: str) -> Tuple[List[Dict[str, str]], List[Dict[str, str]]]: """ - 获取指定chat_id的style和grammar表达方式, 同时获取全局的personality表达方式 + 获取指定chat_id的style和grammar表达方式 返回的每个表达方式字典中都包含了source_id, 用于后续的更新操作 """ learnt_style_expressions = [] learnt_grammar_expressions = [] - personality_expressions = [] # 获取style表达方式 style_dir = os.path.join("data", "expression", "learnt_style", str(chat_id)) @@ -111,19 +108,7 @@ class ExpressionLearner: except Exception as e: logger.error(f"读取grammar表达方式失败: {e}") - # 获取personality表达方式 - personality_file = os.path.join("data", "expression", "personality", "expressions.json") - if os.path.exists(personality_file): - try: - with open(personality_file, "r", encoding="utf-8") as f: - expressions = json.load(f) - for expr in expressions: - expr["source_id"] = "personality" # 添加来源ID - personality_expressions.append(expr) - except Exception as e: - logger.error(f"读取personality表达方式失败: {e}") - - return learnt_style_expressions, learnt_grammar_expressions, personality_expressions + return learnt_style_expressions, learnt_grammar_expressions def is_similar(self, s1: str, s2: str) -> bool: """ @@ -428,6 +413,7 @@ class ExpressionLearner: init_prompt() + expression_learner = None diff --git a/src/chat/focus_chat/heartFC_Cycleinfo.py b/src/chat/focus_chat/heartFC_Cycleinfo.py index 120381df3..f9a90780d 100644 --- a/src/chat/focus_chat/heartFC_Cycleinfo.py +++ b/src/chat/focus_chat/heartFC_Cycleinfo.py @@ -25,7 +25,6 @@ class CycleDetail: self.loop_processor_info: Dict[str, Any] = {} # 前处理器信息 self.loop_plan_info: Dict[str, Any] = {} self.loop_action_info: Dict[str, Any] = {} - self.loop_post_processor_info: Dict[str, Any] = {} # 后处理器信息 def to_dict(self) -> Dict[str, Any]: """将循环信息转换为字典格式""" @@ -80,7 +79,6 @@ class CycleDetail: "loop_processor_info": convert_to_serializable(self.loop_processor_info), "loop_plan_info": convert_to_serializable(self.loop_plan_info), "loop_action_info": convert_to_serializable(self.loop_action_info), - "loop_post_processor_info": convert_to_serializable(self.loop_post_processor_info), } def complete_cycle(self): @@ -135,4 +133,3 @@ class CycleDetail: self.loop_processor_info = loop_info["loop_processor_info"] self.loop_plan_info = loop_info["loop_plan_info"] self.loop_action_info = loop_info["loop_action_info"] - self.loop_post_processor_info = loop_info["loop_post_processor_info"] diff --git a/src/chat/focus_chat/heartFC_chat.py b/src/chat/focus_chat/heartFC_chat.py index ba1222650..a538d9459 100644 --- a/src/chat/focus_chat/heartFC_chat.py +++ b/src/chat/focus_chat/heartFC_chat.py @@ -13,40 +13,32 @@ from src.chat.heart_flow.observation.observation import Observation from src.chat.focus_chat.heartFC_Cycleinfo import CycleDetail from src.chat.focus_chat.info.info_base import InfoBase from src.chat.focus_chat.info_processors.chattinginfo_processor import ChattingInfoProcessor -from src.chat.focus_chat.info_processors.relationship_processor import PersonImpressionpProcessor from src.chat.focus_chat.info_processors.working_memory_processor import WorkingMemoryProcessor from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation from src.chat.heart_flow.observation.working_observation import WorkingMemoryObservation from src.chat.heart_flow.observation.chatting_observation import ChattingObservation -from src.chat.heart_flow.observation.structure_observation import StructureObservation from src.chat.heart_flow.observation.actions_observation import ActionObservation -from src.chat.focus_chat.info_processors.tool_processor import ToolProcessor + from src.chat.focus_chat.memory_activator import MemoryActivator from src.chat.focus_chat.info_processors.base_processor import BaseProcessor -from src.chat.focus_chat.info_processors.expression_selector_processor import ExpressionSelectorProcessor from src.chat.focus_chat.planners.planner_factory import PlannerFactory from src.chat.focus_chat.planners.modify_actions import ActionModifier from src.chat.focus_chat.planners.action_manager import ActionManager from src.config.config import global_config from src.chat.focus_chat.hfc_performance_logger import HFCPerformanceLogger from src.chat.focus_chat.hfc_version_manager import get_hfc_version -from src.chat.focus_chat.info.relation_info import RelationInfo -from src.chat.focus_chat.info.expression_selection_info import ExpressionSelectionInfo -from src.chat.focus_chat.info.structured_info import StructuredInfo +from src.person_info.relationship_builder_manager import relationship_builder_manager install(extra_lines=3) -# 超时常量配置 -MEMORY_ACTIVATION_TIMEOUT = 5.0 # 记忆激活任务超时时限（秒） -ACTION_MODIFICATION_TIMEOUT = 15.0 # 动作修改任务超时时限（秒） +# 注释：原来的动作修改超时常量已移除，因为改为顺序执行 # 定义观察器映射：键是观察器名称，值是 (观察器类, 初始化参数) OBSERVATION_CLASSES = { "ChattingObservation": (ChattingObservation, "chat_id"), "WorkingMemoryObservation": (WorkingMemoryObservation, "observe_id"), "HFCloopObservation": (HFCloopObservation, "observe_id"), - "StructureObservation": (StructureObservation, "observe_id"), } # 定义处理器映射：键是处理器名称，值是 (处理器类, 可选的配置键名) @@ -55,13 +47,6 @@ PROCESSOR_CLASSES = { "WorkingMemoryProcessor": (WorkingMemoryProcessor, "working_memory_processor"), } -# 定义后期处理器映射：在规划后、动作执行前运行的处理器 -POST_PLANNING_PROCESSOR_CLASSES = { - "ToolProcessor": (ToolProcessor, "tool_use_processor"), - "PersonImpressionpProcessor": (PersonImpressionpProcessor, "person_impression_processor"), - "ExpressionSelectorProcessor": (ExpressionSelectorProcessor, "expression_selector_processor"), -} - logger = get_logger("hfc") # Logger Name Changed @@ -112,6 +97,8 @@ class HeartFChatting: self.memory_activator = MemoryActivator() + self.relationship_builder = relationship_builder_manager.get_or_create_builder(self.stream_id) + # 新增：消息计数器和疲惫阈值 self._message_count = 0 # 发送的消息计数 # 基于exit_focus_threshold动态计算疲惫阈值 @@ -124,38 +111,13 @@ class HeartFChatting: self._register_observations() # 根据配置文件和默认规则确定启用的处理器 - config_processor_settings = global_config.focus_chat_processor - self.enabled_processor_names = [] - - for proc_name, (_proc_class, config_key) in PROCESSOR_CLASSES.items(): - # 检查处理器是否应该启用 - if not config_key or getattr(config_processor_settings, config_key, True): - self.enabled_processor_names.append(proc_name) - - # 初始化后期处理器（规划后执行的处理器） - self.enabled_post_planning_processor_names = [] - for proc_name, (_proc_class, config_key) in POST_PLANNING_PROCESSOR_CLASSES.items(): - # 对于关系处理器，需要同时检查两个配置项 - if proc_name == "PersonImpressionpProcessor": - if global_config.relationship.enable_relationship and getattr( - config_processor_settings, config_key, True - ): - self.enabled_post_planning_processor_names.append(proc_name) - else: - # 其他后期处理器的逻辑 - if not config_key or getattr(config_processor_settings, config_key, True): - self.enabled_post_planning_processor_names.append(proc_name) - - # logger.info(f"{self.log_prefix} 将启用的处理器: {self.enabled_processor_names}") - # logger.info(f"{self.log_prefix} 将启用的后期处理器: {self.enabled_post_planning_processor_names}") + self.enabled_processor_names = ["ChattingInfoProcessor"] + if global_config.focus_chat.working_memory_processor: + self.enabled_processor_names.append("WorkingMemoryProcessor") self.processors: List[BaseProcessor] = [] self._register_default_processors() - # 初始化后期处理器 - self.post_planning_processors: List[BaseProcessor] = [] - self._register_post_planning_processors() - self.action_manager = ActionManager() self.action_planner = PlannerFactory.create_planner( log_prefix=self.log_prefix, action_manager=self.action_manager @@ -197,7 +159,7 @@ class HeartFChatting: # 检查是否需要跳过WorkingMemoryObservation if name == "WorkingMemoryObservation": # 如果工作记忆处理器被禁用，则跳过WorkingMemoryObservation - if not global_config.focus_chat_processor.working_memory_processor: + if not global_config.focus_chat.working_memory_processor: logger.debug(f"{self.log_prefix} 工作记忆处理器已禁用，跳过注册观察器 {name}") continue @@ -222,16 +184,12 @@ class HeartFChatting: processor_info = PROCESSOR_CLASSES.get(name) # processor_info is (ProcessorClass, config_key) if processor_info: processor_actual_class = processor_info[0] # 获取实际的类定义 - # 根据处理器类名判断是否需要 subheartflow_id - if name in [ - "WorkingMemoryProcessor", - ]: - self.processors.append(processor_actual_class(subheartflow_id=self.stream_id)) - elif name == "ChattingInfoProcessor": + # 根据处理器类名判断构造参数 + if name == "ChattingInfoProcessor": self.processors.append(processor_actual_class()) + elif name == "WorkingMemoryProcessor": + self.processors.append(processor_actual_class(subheartflow_id=self.stream_id)) else: - # 对于PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器 - # (例如, 新增了一个处理器到PROCESSOR_CLASSES, 它不需要id, 也不叫ChattingInfoProcessor) try: self.processors.append(processor_actual_class()) # 尝试无参构造 logger.debug(f"{self.log_prefix} 注册处理器 {name} (尝试无参构造).") @@ -240,7 +198,6 @@ class HeartFChatting: f"{self.log_prefix} 处理器 {name} 构造失败。它可能需要参数（如 subheartflow_id）但未在注册逻辑中明确处理。" ) else: - # 这理论上不应该发生，因为 enabled_processor_names 是从 PROCESSOR_CLASSES 的键生成的 logger.warning( f"{self.log_prefix} 在 PROCESSOR_CLASSES 中未找到名为 '{name}' 的处理器定义，将跳过注册。" ) @@ -250,46 +207,6 @@ class HeartFChatting: else: logger.warning(f"{self.log_prefix} 没有注册任何处理器。这可能是由于配置错误或所有处理器都被禁用了。") - def _register_post_planning_processors(self): - """根据 self.enabled_post_planning_processor_names 注册后期处理器""" - self.post_planning_processors = [] # 清空已有的 - - for name in self.enabled_post_planning_processor_names: # 'name' is "PersonImpressionpProcessor", etc. - processor_info = POST_PLANNING_PROCESSOR_CLASSES.get(name) # processor_info is (ProcessorClass, config_key) - if processor_info: - processor_actual_class = processor_info[0] # 获取实际的类定义 - # 根据处理器类名判断是否需要 subheartflow_id - if name in [ - "ToolProcessor", - "PersonImpressionpProcessor", - "ExpressionSelectorProcessor", - ]: - self.post_planning_processors.append(processor_actual_class(subheartflow_id=self.stream_id)) - else: - # 对于POST_PLANNING_PROCESSOR_CLASSES中定义但此处未明确处理构造的处理器 - # (例如, 新增了一个处理器到POST_PLANNING_PROCESSOR_CLASSES, 它不需要id, 也不叫PersonImpressionpProcessor) - try: - self.post_planning_processors.append(processor_actual_class()) # 尝试无参构造 - logger.debug(f"{self.log_prefix} 注册后期处理器 {name} (尝试无参构造).") - except TypeError: - logger.error( - f"{self.log_prefix} 后期处理器 {name} 构造失败。它可能需要参数（如 subheartflow_id）但未在注册逻辑中明确处理。" - ) - else: - # 这理论上不应该发生，因为 enabled_post_planning_processor_names 是从 POST_PLANNING_PROCESSOR_CLASSES 的键生成的 - logger.warning( - f"{self.log_prefix} 在 POST_PLANNING_PROCESSOR_CLASSES 中未找到名为 '{name}' 的处理器定义，将跳过注册。" - ) - - if self.post_planning_processors: - logger.info( - f"{self.log_prefix} 已注册后期处理器: {[p.__class__.__name__ for p in self.post_planning_processors]}" - ) - else: - logger.warning( - f"{self.log_prefix} 没有注册任何后期处理器。这可能是由于配置错误或所有后期处理器都被禁用了。" - ) - async def start(self): """检查是否需要启动主循环，如果未激活则启动。""" logger.debug(f"{self.log_prefix} 开始启动 HeartFChatting") @@ -470,27 +387,12 @@ class HeartFChatting: ("\n前处理器耗时: " + "; ".join(processor_time_strings)) if processor_time_strings else "" ) - # 新增：输出每个后处理器的耗时 - post_processor_time_costs = self._current_cycle_detail.loop_post_processor_info.get( - "post_processor_time_costs", {} - ) - post_processor_time_strings = [] - for pname, ptime in post_processor_time_costs.items(): - formatted_ptime = f"{ptime * 1000:.2f}毫秒" if ptime < 1 else f"{ptime:.2f}秒" - post_processor_time_strings.append(f"{pname}: {formatted_ptime}") - post_processor_time_log = ( - ("\n后处理器耗时: " + "; ".join(post_processor_time_strings)) - if post_processor_time_strings - else "" - ) - logger.info( f"{self.log_prefix} 第{self._current_cycle_detail.cycle_id}次思考," f"耗时: {self._current_cycle_detail.end_time - self._current_cycle_detail.start_time:.1f}秒, " f"动作: {self._current_cycle_detail.loop_plan_info.get('action_result', {}).get('action_type', '未知动作')}" + (f"\n详情: {'; '.join(timer_strings)}" if timer_strings else "") + processor_time_log - + post_processor_time_log ) # 记录性能数据 @@ -501,8 +403,7 @@ class HeartFChatting: "action_type": action_result.get("action_type", "unknown"), "total_time": self._current_cycle_detail.end_time - self._current_cycle_detail.start_time, "step_times": cycle_timers.copy(), - "processor_time_costs": processor_time_costs, # 前处理器时间 - "post_processor_time_costs": post_processor_time_costs, # 后处理器时间 + "processor_time_costs": processor_time_costs, # 处理器时间 "reasoning": action_result.get("reasoning", ""), "success": self._current_cycle_detail.loop_action_info.get("action_taken", False), } @@ -589,10 +490,7 @@ class HeartFChatting: processor_name = processor.__class__.log_prefix async def run_with_timeout(proc=processor): - return await asyncio.wait_for( - proc.process_info(observations=observations), - timeout=global_config.focus_chat.processor_max_time, - ) + return await asyncio.wait_for(proc.process_info(observations=observations), 30) task = asyncio.create_task(run_with_timeout()) @@ -621,10 +519,8 @@ class HeartFChatting: # 记录耗时 processor_time_costs[processor_name] = duration_since_parallel_start except asyncio.TimeoutError: - logger.info( - f"{self.log_prefix} 处理器 {processor_name} 超时（>{global_config.focus_chat.processor_max_time}s），已跳过" - ) - processor_time_costs[processor_name] = global_config.focus_chat.processor_max_time + logger.info(f"{self.log_prefix} 处理器 {processor_name} 超时（>30s），已跳过") + processor_time_costs[processor_name] = 30 except Exception as e: logger.error( f"{self.log_prefix} 处理器 {processor_name} 执行失败，耗时 (自并行开始): {duration_since_parallel_start:.2f}秒. 错误: {e}", @@ -649,190 +545,6 @@ class HeartFChatting: return all_plan_info, processor_time_costs - async def _process_post_planning_processors_with_timing( - self, observations: List[Observation], action_type: str, action_data: dict - ) -> tuple[dict, dict]: - """ - 处理后期处理器（规划后执行的处理器）并收集详细时间统计 - 包括：关系处理器、表达选择器、记忆激活器 - - 参数: - observations: 观察器列表 - action_type: 动作类型 - action_data: 原始动作数据 - - 返回: - tuple[dict, dict]: (更新后的动作数据, 后处理器时间统计) - """ - logger.info(f"{self.log_prefix} 开始执行后期处理器（带详细统计）") - - # 创建所有后期任务 - task_list = [] - task_to_name_map = {} - task_start_times = {} - post_processor_time_costs = {} - - # 添加后期处理器任务 - for processor in self.post_planning_processors: - processor_name = processor.__class__.__name__ - - async def run_processor_with_timeout_and_timing(proc=processor, name=processor_name): - start_time = time.time() - try: - result = await asyncio.wait_for( - proc.process_info(observations=observations, action_type=action_type, action_data=action_data), - timeout=global_config.focus_chat.processor_max_time, - ) - end_time = time.time() - post_processor_time_costs[name] = end_time - start_time - logger.debug(f"{self.log_prefix} 后期处理器 {name} 耗时: {end_time - start_time:.3f}秒") - return result - except Exception as e: - end_time = time.time() - post_processor_time_costs[name] = end_time - start_time - logger.warning(f"{self.log_prefix} 后期处理器 {name} 执行异常，耗时: {end_time - start_time:.3f}秒") - raise e - - task = asyncio.create_task(run_processor_with_timeout_and_timing()) - task_list.append(task) - task_to_name_map[task] = ("processor", processor_name) - task_start_times[task] = time.time() - logger.info(f"{self.log_prefix} 启动后期处理器任务: {processor_name}") - - # 添加记忆激活器任务 - async def run_memory_with_timeout_and_timing(): - start_time = time.time() - try: - result = await asyncio.wait_for( - self.memory_activator.activate_memory(observations), - timeout=MEMORY_ACTIVATION_TIMEOUT, - ) - end_time = time.time() - post_processor_time_costs["MemoryActivator"] = end_time - start_time - logger.debug(f"{self.log_prefix} 记忆激活器耗时: {end_time - start_time:.3f}秒") - return result - except Exception as e: - end_time = time.time() - post_processor_time_costs["MemoryActivator"] = end_time - start_time - logger.warning(f"{self.log_prefix} 记忆激活器执行异常，耗时: {end_time - start_time:.3f}秒") - raise e - - memory_task = asyncio.create_task(run_memory_with_timeout_and_timing()) - task_list.append(memory_task) - task_to_name_map[memory_task] = ("memory", "MemoryActivator") - task_start_times[memory_task] = time.time() - logger.info(f"{self.log_prefix} 启动记忆激活器任务") - - # 如果没有任何后期任务，直接返回 - if not task_list: - logger.info(f"{self.log_prefix} 没有启用的后期处理器或记忆激活器") - return action_data, {} - - # 等待所有任务完成 - pending_tasks = set(task_list) - all_post_plan_info = [] - running_memorys = [] - - while pending_tasks: - done, pending_tasks = await asyncio.wait(pending_tasks, return_when=asyncio.FIRST_COMPLETED) - - for task in done: - task_type, task_name = task_to_name_map[task] - - try: - result = await task - - if task_type == "processor": - logger.info(f"{self.log_prefix} 后期处理器 {task_name} 已完成!") - if result is not None: - all_post_plan_info.extend(result) - else: - logger.warning(f"{self.log_prefix} 后期处理器 {task_name} 返回了 None") - elif task_type == "memory": - logger.info(f"{self.log_prefix} 记忆激活器已完成!") - if result is not None: - running_memorys = result - else: - logger.warning(f"{self.log_prefix} 记忆激活器返回了 None") - running_memorys = [] - - except asyncio.TimeoutError: - # 对于超时任务，记录已用时间 - elapsed_time = time.time() - task_start_times[task] - if task_type == "processor": - post_processor_time_costs[task_name] = elapsed_time - logger.warning( - f"{self.log_prefix} 后期处理器 {task_name} 超时（>{global_config.focus_chat.processor_max_time}s），已跳过，耗时: {elapsed_time:.3f}秒" - ) - elif task_type == "memory": - post_processor_time_costs["MemoryActivator"] = elapsed_time - logger.warning( - f"{self.log_prefix} 记忆激活器超时（>{MEMORY_ACTIVATION_TIMEOUT}s），已跳过，耗时: {elapsed_time:.3f}秒" - ) - running_memorys = [] - except Exception as e: - # 对于异常任务，记录已用时间 - elapsed_time = time.time() - task_start_times[task] - if task_type == "processor": - post_processor_time_costs[task_name] = elapsed_time - logger.error( - f"{self.log_prefix} 后期处理器 {task_name} 执行失败，耗时: {elapsed_time:.3f}秒. 错误: {e}", - exc_info=True, - ) - elif task_type == "memory": - post_processor_time_costs["MemoryActivator"] = elapsed_time - logger.error( - f"{self.log_prefix} 记忆激活器执行失败，耗时: {elapsed_time:.3f}秒. 错误: {e}", - exc_info=True, - ) - running_memorys = [] - - # 将后期处理器的结果整合到 action_data 中 - updated_action_data = action_data.copy() - - relation_info = "" - selected_expressions = [] - structured_info = "" - - for info in all_post_plan_info: - if isinstance(info, RelationInfo): - relation_info = info.get_processed_info() - elif isinstance(info, ExpressionSelectionInfo): - selected_expressions = info.get_expressions_for_action_data() - elif isinstance(info, StructuredInfo): - structured_info = info.get_processed_info() - - if relation_info: - updated_action_data["relation_info_block"] = relation_info - - if selected_expressions: - updated_action_data["selected_expressions"] = selected_expressions - - if structured_info: - updated_action_data["structured_info"] = structured_info - - # 特殊处理running_memorys - if running_memorys: - memory_str = "以下是当前在聊天中，你回忆起的记忆：\n" - for running_memory in running_memorys: - memory_str += f"{running_memory['content']}\n" - updated_action_data["memory_block"] = memory_str - logger.info(f"{self.log_prefix} 添加了 {len(running_memorys)} 个激活的记忆到action_data") - - if all_post_plan_info or running_memorys: - logger.info( - f"{self.log_prefix} 后期处理完成，产生了 {len(all_post_plan_info)} 个信息项和 {len(running_memorys)} 个记忆" - ) - - # 输出详细统计信息 - if post_processor_time_costs: - stats_str = ", ".join( - [f"{name}: {time_cost:.3f}s" for name, time_cost in post_processor_time_costs.items()] - ) - logger.info(f"{self.log_prefix} 后期处理器详细耗时统计: {stats_str}") - - return updated_action_data, post_processor_time_costs - async def _observe_process_plan_action_loop(self, cycle_timers: dict, thinking_id: str) -> dict: try: loop_start_time = time.time() @@ -845,12 +557,12 @@ class HeartFChatting: "observations": self.observations, } - # 根据配置决定是否并行执行调整动作、回忆和处理器阶段 + await self.relationship_builder.build_relation() - # 并行执行调整动作、回忆和处理器阶段 - with Timer("并行调整动作、处理", cycle_timers): - # 创建并行任务 - async def modify_actions_task(): + # 顺序执行调整动作和处理器阶段 + # 第一步：动作修改 + with Timer("动作修改", cycle_timers): + try: # 调用完整的动作修改流程 await self.action_modifier.modify_actions( observations=self.observations, @@ -858,44 +570,17 @@ class HeartFChatting: await self.action_observation.observe() self.observations.append(self.action_observation) - return True - - # 创建两个并行任务，为LLM调用添加超时保护 - action_modify_task = asyncio.create_task( - asyncio.wait_for(modify_actions_task(), timeout=ACTION_MODIFICATION_TIMEOUT) - ) - processor_task = asyncio.create_task(self._process_processors(self.observations)) - - # 等待两个任务完成，使用超时保护和详细错误处理 - action_modify_result = None - all_plan_info = [] - processor_time_costs = {} - - try: - action_modify_result, (all_plan_info, processor_time_costs) = await asyncio.gather( - action_modify_task, processor_task, return_exceptions=True - ) - - # 检查各个任务的结果 - if isinstance(action_modify_result, Exception): - if isinstance(action_modify_result, asyncio.TimeoutError): - logger.error(f"{self.log_prefix} 动作修改任务超时") - else: - logger.error(f"{self.log_prefix} 动作修改任务失败: {action_modify_result}") - - processor_result = (all_plan_info, processor_time_costs) - if isinstance(processor_result, Exception): - if isinstance(processor_result, asyncio.TimeoutError): - logger.error(f"{self.log_prefix} 处理器任务超时") - else: - logger.error(f"{self.log_prefix} 处理器任务失败: {processor_result}") - all_plan_info = [] - processor_time_costs = {} - else: - all_plan_info, processor_time_costs = processor_result - + logger.debug(f"{self.log_prefix} 动作修改完成") except Exception as e: - logger.error(f"{self.log_prefix} 并行任务gather失败: {e}") + logger.error(f"{self.log_prefix} 动作修改失败: {e}") + # 继续执行，不中断流程 + + # 第二步：信息处理器 + with Timer("信息处理器", cycle_timers): + try: + all_plan_info, processor_time_costs = await self._process_processors(self.observations) + except Exception as e: + logger.error(f"{self.log_prefix} 信息处理器失败: {e}") # 设置默认值以继续执行 all_plan_info = [] processor_time_costs = {} @@ -908,14 +593,13 @@ class HeartFChatting: logger.debug(f"{self.log_prefix} 并行阶段完成，准备进入规划器，plan_info数量: {len(all_plan_info)}") with Timer("规划器", cycle_timers): - plan_result = await self.action_planner.plan(all_plan_info, [], loop_start_time) + plan_result = await self.action_planner.plan(all_plan_info, self.observations, loop_start_time) loop_plan_info = { "action_result": plan_result.get("action_result", {}), "observed_messages": plan_result.get("observed_messages", ""), } - # 修正：将后期处理器从执行动作Timer中分离出来 action_type, action_data, reasoning = ( plan_result.get("action_result", {}).get("action_type", "error"), plan_result.get("action_result", {}).get("action_data", {}), @@ -931,22 +615,7 @@ class HeartFChatting: logger.debug(f"{self.log_prefix} 麦麦想要：'{action_str}'") - # 添加：单独计时后期处理器，并收集详细统计 - post_processor_time_costs = {} - if action_type != "no_reply": - with Timer("后期处理器", cycle_timers): - logger.debug(f"{self.log_prefix} 执行后期处理器（动作类型: {action_type}）") - # 记录详细的后处理器时间 - post_start_time = time.time() - action_data, post_processor_time_costs = await self._process_post_planning_processors_with_timing( - self.observations, action_type, action_data - ) - post_end_time = time.time() - logger.info(f"{self.log_prefix} 后期处理器总耗时: {post_end_time - post_start_time:.3f}秒") - else: - logger.debug(f"{self.log_prefix} 跳过后期处理器（动作类型: {action_type}）") - - # 修正：纯动作执行计时 + # 动作执行计时 with Timer("动作执行", cycle_timers): success, reply_text, command = await self._handle_action( action_type, reasoning, action_data, cycle_timers, thinking_id @@ -959,17 +628,11 @@ class HeartFChatting: "taken_time": time.time(), } - # 添加后处理器统计到loop_info - loop_post_processor_info = { - "post_processor_time_costs": post_processor_time_costs, - } - loop_info = { "loop_observation_info": loop_observation_info, "loop_processor_info": loop_processor_info, "loop_plan_info": loop_plan_info, "loop_action_info": loop_action_info, - "loop_post_processor_info": loop_post_processor_info, # 新增 } return loop_info diff --git a/src/chat/focus_chat/heartflow_message_processor.py b/src/chat/focus_chat/heartflow_message_processor.py index d7299d4c6..56f4a73e2 100644 --- a/src/chat/focus_chat/heartflow_message_processor.py +++ b/src/chat/focus_chat/heartflow_message_processor.py @@ -3,16 +3,14 @@ from src.config.config import global_config from src.chat.message_receive.message import MessageRecv from src.chat.message_receive.storage import MessageStorage from src.chat.heart_flow.heartflow import heartflow -from src.chat.message_receive.chat_stream import get_chat_manager, ChatStream +from src.chat.message_receive.chat_stream import get_chat_manager from src.chat.utils.utils import is_mentioned_bot_in_message from src.chat.utils.timer_calculator import Timer from src.common.logger import get_logger - -import math import re +import math import traceback from typing import Optional, Tuple -from maim_message import UserInfo from src.person_info.relationship_manager import get_relationship_manager @@ -90,46 +88,6 @@ async def _calculate_interest(message: MessageRecv) -> Tuple[float, bool]: return interested_rate, is_mentioned -def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool: - """检查消息是否包含过滤词 - - Args: - text: 待检查的文本 - chat: 聊天对象 - userinfo: 用户信息 - - Returns: - bool: 是否包含过滤词 - """ - for word in global_config.message_receive.ban_words: - if word in text: - chat_name = chat.group_info.group_name if chat.group_info else "私聊" - logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}") - logger.info(f"[过滤词识别]消息中含有{word}，filtered") - return True - return False - - -def _check_ban_regex(text: str, chat: ChatStream, userinfo: UserInfo) -> bool: - """检查消息是否匹配过滤正则表达式 - - Args: - text: 待检查的文本 - chat: 聊天对象 - userinfo: 用户信息 - - Returns: - bool: 是否匹配过滤正则 - """ - for pattern in global_config.message_receive.ban_msgs_regex: - if re.search(pattern, text): - chat_name = chat.group_info.group_name if chat.group_info else "私聊" - logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}") - logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered") - return True - return False - - class HeartFCMessageReceiver: """心流处理器，负责处理接收到的消息并计算兴趣度""" @@ -167,12 +125,6 @@ class HeartFCMessageReceiver: subheartflow = await heartflow.get_or_create_subheartflow(chat.stream_id) message.update_chat_stream(chat) - # 3. 过滤检查 - if _check_ban_words(message.processed_plain_text, chat, userinfo) or _check_ban_regex( - message.raw_message, chat, userinfo - ): - return - # 6. 兴趣度计算与更新 interested_rate, is_mentioned = await _calculate_interest(message) subheartflow.add_message_to_normal_chat_cache(message, interested_rate, is_mentioned) @@ -183,7 +135,6 @@ class HeartFCMessageReceiver: current_talk_frequency = global_config.chat.get_current_talk_frequency(chat.stream_id) # 如果消息中包含图片标识，则日志展示为图片 - import re picid_match = re.search(r"\[picid:([^\]]+)\]", message.processed_plain_text) if picid_match: diff --git a/src/chat/focus_chat/hfc_performance_logger.py b/src/chat/focus_chat/hfc_performance_logger.py index 2b7f44070..7ae3ea2de 100644 --- a/src/chat/focus_chat/hfc_performance_logger.py +++ b/src/chat/focus_chat/hfc_performance_logger.py @@ -42,7 +42,6 @@ class HFCPerformanceLogger: "total_time": cycle_data.get("total_time", 0), "step_times": cycle_data.get("step_times", {}), "processor_time_costs": cycle_data.get("processor_time_costs", {}), # 前处理器时间 - "post_processor_time_costs": cycle_data.get("post_processor_time_costs", {}), # 后处理器时间 "reasoning": cycle_data.get("reasoning", ""), "success": cycle_data.get("success", False), } @@ -60,13 +59,6 @@ class HFCPerformanceLogger: f"time={record['total_time']:.2f}s", ] - # 添加后处理器时间信息到日志 - if record["post_processor_time_costs"]: - post_processor_stats = ", ".join( - [f"{name}: {time_cost:.3f}s" for name, time_cost in record["post_processor_time_costs"].items()] - ) - log_parts.append(f"post_processors=({post_processor_stats})") - logger.debug(f"记录HFC循环数据: {', '.join(log_parts)}") except Exception as e: diff --git a/src/chat/focus_chat/hfc_version_manager.py b/src/chat/focus_chat/hfc_version_manager.py index bccc9e22a..91a3f51be 100644 --- a/src/chat/focus_chat/hfc_version_manager.py +++ b/src/chat/focus_chat/hfc_version_manager.py @@ -20,7 +20,7 @@ class HFCVersionManager: """HFC版本号管理器""" # 默认版本号 - DEFAULT_VERSION = "v4.0.0" + DEFAULT_VERSION = "v5.0.0" # 当前运行时版本号 _current_version: Optional[str] = None diff --git a/src/chat/focus_chat/info/expression_selection_info.py b/src/chat/focus_chat/info/expression_selection_info.py deleted file mode 100644 index 9eaa0f4e0..000000000 --- a/src/chat/focus_chat/info/expression_selection_info.py +++ /dev/null @@ -1,71 +0,0 @@ -from dataclasses import dataclass -from typing import List, Dict -from .info_base import InfoBase - - -@dataclass -class ExpressionSelectionInfo(InfoBase): - """表达选择信息类 - - 用于存储和管理选中的表达方式信息。 - - Attributes: - type (str): 信息类型标识符，默认为 "expression_selection" - data (Dict[str, Any]): 包含选中表达方式的数据字典 - """ - - type: str = "expression_selection" - - def get_selected_expressions(self) -> List[Dict[str, str]]: - """获取选中的表达方式列表 - - Returns: - List[Dict[str, str]]: 选中的表达方式列表 - """ - return self.get_info("selected_expressions") or [] - - def set_selected_expressions(self, expressions: List[Dict[str, str]]) -> None: - """设置选中的表达方式列表 - - Args: - expressions: 选中的表达方式列表 - """ - self.data["selected_expressions"] = expressions - - def get_expressions_count(self) -> int: - """获取选中表达方式的数量 - - Returns: - int: 表达方式数量 - """ - return len(self.get_selected_expressions()) - - def get_processed_info(self) -> str: - """获取处理后的信息 - - Returns: - str: 处理后的信息字符串 - """ - expressions = self.get_selected_expressions() - if not expressions: - return "" - - # 格式化表达方式为可读文本 - formatted_expressions = [] - for expr in expressions: - situation = expr.get("situation", "") - style = expr.get("style", "") - expr.get("type", "") - - if situation and style: - formatted_expressions.append(f"当{situation}时，使用 {style}") - - return "\n".join(formatted_expressions) - - def get_expressions_for_action_data(self) -> List[Dict[str, str]]: - """获取用于action_data的表达方式数据 - - Returns: - List[Dict[str, str]]: 格式化后的表达方式数据 - """ - return self.get_selected_expressions() diff --git a/src/chat/focus_chat/info/mind_info.py b/src/chat/focus_chat/info/mind_info.py deleted file mode 100644 index 3cfde1bbb..000000000 --- a/src/chat/focus_chat/info/mind_info.py +++ /dev/null @@ -1,34 +0,0 @@ -from typing import Dict, Any -from dataclasses import dataclass, field -from .info_base import InfoBase - - -@dataclass -class MindInfo(InfoBase): - """思维信息类 - - 用于存储和管理当前思维状态的信息。 - - Attributes: - type (str): 信息类型标识符，默认为 "mind" - data (Dict[str, Any]): 包含 current_mind 的数据字典 - """ - - type: str = "mind" - data: Dict[str, Any] = field(default_factory=lambda: {"current_mind": ""}) - - def get_current_mind(self) -> str: - """获取当前思维状态 - - Returns: - str: 当前思维状态 - """ - return self.get_info("current_mind") or "" - - def set_current_mind(self, mind: str) -> None: - """设置当前思维状态 - - Args: - mind: 要设置的思维状态 - """ - self.data["current_mind"] = mind diff --git a/src/chat/focus_chat/info/relation_info.py b/src/chat/focus_chat/info/relation_info.py deleted file mode 100644 index 0e4ea9533..000000000 --- a/src/chat/focus_chat/info/relation_info.py +++ /dev/null @@ -1,40 +0,0 @@ -from dataclasses import dataclass -from .info_base import InfoBase - - -@dataclass -class RelationInfo(InfoBase): - """关系信息类 - - 用于存储和管理当前关系状态的信息。 - - Attributes: - type (str): 信息类型标识符，默认为 "relation" - data (Dict[str, Any]): 包含 current_relation 的数据字典 - """ - - type: str = "relation" - - def get_relation_info(self) -> str: - """获取当前关系状态 - - Returns: - str: 当前关系状态 - """ - return self.get_info("relation_info") or "" - - def set_relation_info(self, relation_info: str) -> None: - """设置当前关系状态 - - Args: - relation_info: 要设置的关系状态 - """ - self.data["relation_info"] = relation_info - - def get_processed_info(self) -> str: - """获取处理后的信息 - - Returns: - str: 处理后的信息 - """ - return self.get_relation_info() or "" diff --git a/src/chat/focus_chat/info/structured_info.py b/src/chat/focus_chat/info/structured_info.py deleted file mode 100644 index a925a6d17..000000000 --- a/src/chat/focus_chat/info/structured_info.py +++ /dev/null @@ -1,85 +0,0 @@ -from typing import Dict, Optional, Any, List -from dataclasses import dataclass, field - - -@dataclass -class StructuredInfo: - """信息基类 - - 这是一个基础信息类，用于存储和管理各种类型的信息数据。 - 所有具体的信息类都应该继承自这个基类。 - - Attributes: - type (str): 信息类型标识符，默认为 "base" - data (Dict[str, Union[str, Dict, list]]): 存储具体信息数据的字典， - 支持存储字符串、字典、列表等嵌套数据结构 - """ - - type: str = "structured_info" - data: Dict[str, Any] = field(default_factory=dict) - - def get_type(self) -> str: - """获取信息类型 - - Returns: - str: 当前信息对象的类型标识符 - """ - return self.type - - def get_data(self) -> Dict[str, Any]: - """获取所有信息数据 - - Returns: - Dict[str, Any]: 包含所有信息数据的字典 - """ - return self.data - - def get_info(self, key: str) -> Optional[Any]: - """获取特定属性的信息 - - Args: - key: 要获取的属性键名 - - Returns: - Optional[Any]: 属性值，如果键不存在则返回 None - """ - return self.data.get(key) - - def get_info_list(self, key: str) -> List[Any]: - """获取特定属性的信息列表 - - Args: - key: 要获取的属性键名 - - Returns: - List[Any]: 属性值列表，如果键不存在则返回空列表 - """ - value = self.data.get(key) - if isinstance(value, list): - return value - return [] - - def set_info(self, key: str, value: Any) -> None: - """设置特定属性的信息值 - - Args: - key: 要设置的属性键名 - value: 要设置的属性值 - """ - self.data[key] = value - - def get_processed_info(self) -> str: - """获取处理后的信息 - - Returns: - str: 处理后的信息字符串 - """ - - info_str = "" - # print(f"self.data: {self.data}") - - for key, value in self.data.items(): - # print(f"key: {key}, value: {value}") - info_str += f"信息类型：{key}，信息内容：{value}\n" - - return info_str diff --git a/src/chat/focus_chat/info_processors/expression_selector_processor.py b/src/chat/focus_chat/info_processors/expression_selector_processor.py deleted file mode 100644 index 66b199718..000000000 --- a/src/chat/focus_chat/info_processors/expression_selector_processor.py +++ /dev/null @@ -1,107 +0,0 @@ -import time -import random -from typing import List -from src.chat.heart_flow.observation.chatting_observation import ChattingObservation -from src.chat.heart_flow.observation.observation import Observation -from src.common.logger import get_logger -from src.chat.message_receive.chat_stream import get_chat_manager -from .base_processor import BaseProcessor -from src.chat.focus_chat.info.info_base import InfoBase -from src.chat.focus_chat.info.expression_selection_info import ExpressionSelectionInfo -from src.chat.express.expression_selector import expression_selector - -logger = get_logger("processor") - - -class ExpressionSelectorProcessor(BaseProcessor): - log_prefix = "表达选择器" - - def __init__(self, subheartflow_id: str): - super().__init__() - - self.subheartflow_id = subheartflow_id - self.last_selection_time = 0 - self.selection_interval = 10 # 40秒间隔 - self.cached_expressions = [] # 缓存上一次选择的表达方式 - - name = get_chat_manager().get_stream_name(self.subheartflow_id) - self.log_prefix = f"[{name}] 表达选择器" - - async def process_info( - self, - observations: List[Observation] = None, - action_type: str = None, - action_data: dict = None, - **kwargs, - ) -> List[InfoBase]: - """处理信息对象 - - Args: - observations: 观察对象列表 - - Returns: - List[InfoBase]: 处理后的表达选择信息列表 - """ - current_time = time.time() - - # 检查频率限制 - if current_time - self.last_selection_time < self.selection_interval: - logger.debug(f"{self.log_prefix} 距离上次选择不足{self.selection_interval}秒，使用缓存的表达方式") - # 使用缓存的表达方式 - if self.cached_expressions: - # 从缓存的15个中随机选5个 - final_expressions = random.sample(self.cached_expressions, min(5, len(self.cached_expressions))) - - # 创建表达选择信息 - expression_info = ExpressionSelectionInfo() - expression_info.set_selected_expressions(final_expressions) - - logger.info(f"{self.log_prefix} 使用缓存选择了{len(final_expressions)}个表达方式") - return [expression_info] - else: - logger.debug(f"{self.log_prefix} 没有缓存的表达方式，跳过选择") - return [] - - # 获取聊天内容 - chat_info = "" - if observations: - for observation in observations: - if isinstance(observation, ChattingObservation): - # chat_info = observation.get_observe_info() - chat_info = observation.talking_message_str_truncate_short - break - - if not chat_info: - logger.debug(f"{self.log_prefix} 没有聊天内容，跳过表达方式选择") - return [] - - try: - if action_type == "reply": - target_message = action_data.get("reply_to", "") - else: - target_message = "" - - # LLM模式：调用LLM选择5-10个，然后随机选5个 - selected_expressions = await expression_selector.select_suitable_expressions_llm( - self.subheartflow_id, chat_info, max_num=12, min_num=2, target_message=target_message - ) - cache_size = len(selected_expressions) if selected_expressions else 0 - mode_desc = f"LLM模式（已缓存{cache_size}个）" - - if selected_expressions: - self.cached_expressions = selected_expressions - self.last_selection_time = current_time - - # 创建表达选择信息 - expression_info = ExpressionSelectionInfo() - expression_info.set_selected_expressions(selected_expressions) - - logger.info(f"{self.log_prefix} 为当前聊天选择了{len(selected_expressions)}个表达方式（{mode_desc}）") - return [expression_info] - else: - logger.debug(f"{self.log_prefix} 未选择任何表达方式") - return [] - - except Exception as e: - logger.error(f"{self.log_prefix} 处理表达方式选择时出错: {e}") - return [] diff --git a/src/chat/focus_chat/info_processors/relationship_processor.py b/src/chat/focus_chat/info_processors/relationship_processor.py deleted file mode 100644 index e16def9fe..000000000 --- a/src/chat/focus_chat/info_processors/relationship_processor.py +++ /dev/null @@ -1,951 +0,0 @@ -from src.chat.heart_flow.observation.chatting_observation import ChattingObservation -from src.chat.heart_flow.observation.observation import Observation -from src.llm_models.utils_model import LLMRequest -from src.config.config import global_config -import time -import traceback -from src.common.logger import get_logger -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.chat.message_receive.chat_stream import get_chat_manager -from src.person_info.relationship_manager import get_relationship_manager -from .base_processor import BaseProcessor -from typing import List -from typing import Dict -from src.chat.focus_chat.info.info_base import InfoBase -from src.chat.focus_chat.info.relation_info import RelationInfo -from json_repair import repair_json -from src.person_info.person_info import get_person_info_manager -import json -import asyncio -from src.chat.utils.chat_message_builder import ( - get_raw_msg_by_timestamp_with_chat, - get_raw_msg_by_timestamp_with_chat_inclusive, - get_raw_msg_before_timestamp_with_chat, - num_new_messages_since, -) -import os -import pickle - - -# 消息段清理配置 -SEGMENT_CLEANUP_CONFIG = { - "enable_cleanup": True, # 是否启用清理 - "max_segment_age_days": 7, # 消息段最大保存天数 - "max_segments_per_user": 10, # 每用户最大消息段数 - "cleanup_interval_hours": 1, # 清理间隔（小时） -} - - -logger = get_logger("processor") - - -def init_prompt(): - relationship_prompt = """ -<聊天记录> -{chat_observe_info} - - -{name_block} -现在，你想要回复{person_name}的消息，消息内容是：{target_message}。请根据聊天记录和你要回复的消息，从你对{person_name}的了解中提取有关的信息： -1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，对ta的印象，最近发生的事等等。 -2.请注意，请不要重复调取相同的信息，已经调取的信息如下： -{info_cache_block} -3.如果当前聊天记录中没有需要查询的信息，或者现有信息已经足够回复，请返回{{"none": "不需要查询"}} - -请以json格式输出，例如： - -{{ - "info_type": "信息类型", -}} - -请严格按照json输出格式，不要输出多余内容： -""" - Prompt(relationship_prompt, "relationship_prompt") - - fetch_info_prompt = """ - -{name_block} -以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解： -{person_impression_block} -{points_text_block} - -请从中提取用户"{person_name}"的有关"{info_type}"信息 -请以json格式输出，例如： - -{{ - {info_json_str} -}} - -请严格按照json输出格式，不要输出多余内容： -""" - Prompt(fetch_info_prompt, "fetch_person_info_prompt") - - -class PersonImpressionpProcessor(BaseProcessor): - log_prefix = "关系" - - def __init__(self, subheartflow_id: str): - super().__init__() - - self.subheartflow_id = subheartflow_id - self.info_fetching_cache: List[Dict[str, any]] = [] - self.info_fetched_cache: Dict[ - str, Dict[str, any] - ] = {} # {person_id: {"info": str, "ttl": int, "start_time": float}} - - # 新的消息段缓存结构： - # {person_id: [{"start_time": float, "end_time": float, "last_msg_time": float, "message_count": int}, ...]} - self.person_engaged_cache: Dict[str, List[Dict[str, any]]] = {} - - # 持久化存储文件路径 - self.cache_file_path = os.path.join("data", "relationship", f"relationship_cache_{self.subheartflow_id}.pkl") - - # 最后处理的消息时间，避免重复处理相同消息 - current_time = time.time() - self.last_processed_message_time = current_time - - # 最后清理时间，用于定期清理老消息段 - self.last_cleanup_time = 0.0 - - self.llm_model = LLMRequest( - model=global_config.model.relation, - request_type="focus.relationship", - ) - - # 小模型用于即时信息提取 - self.instant_llm_model = LLMRequest( - model=global_config.model.utils_small, - request_type="focus.relationship.instant", - ) - - name = get_chat_manager().get_stream_name(self.subheartflow_id) - self.log_prefix = f"[{name}] " - - # 加载持久化的缓存 - self._load_cache() - - # ================================ - # 缓存管理模块 - # 负责持久化存储、状态管理、缓存读写 - # ================================ - - def _load_cache(self): - """从文件加载持久化的缓存""" - if os.path.exists(self.cache_file_path): - try: - with open(self.cache_file_path, "rb") as f: - cache_data = pickle.load(f) - # 新格式：包含额外信息的缓存 - self.person_engaged_cache = cache_data.get("person_engaged_cache", {}) - self.last_processed_message_time = cache_data.get("last_processed_message_time", 0.0) - self.last_cleanup_time = cache_data.get("last_cleanup_time", 0.0) - - logger.info( - f"{self.log_prefix} 成功加载关系缓存，包含 {len(self.person_engaged_cache)} 个用户，最后处理时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.last_processed_message_time)) if self.last_processed_message_time > 0 else '未设置'}" - ) - except Exception as e: - logger.error(f"{self.log_prefix} 加载关系缓存失败: {e}") - self.person_engaged_cache = {} - self.last_processed_message_time = 0.0 - else: - logger.info(f"{self.log_prefix} 关系缓存文件不存在，使用空缓存") - - def _save_cache(self): - """保存缓存到文件""" - try: - os.makedirs(os.path.dirname(self.cache_file_path), exist_ok=True) - cache_data = { - "person_engaged_cache": self.person_engaged_cache, - "last_processed_message_time": self.last_processed_message_time, - "last_cleanup_time": self.last_cleanup_time, - } - with open(self.cache_file_path, "wb") as f: - pickle.dump(cache_data, f) - logger.debug(f"{self.log_prefix} 成功保存关系缓存") - except Exception as e: - logger.error(f"{self.log_prefix} 保存关系缓存失败: {e}") - - # ================================ - # 消息段管理模块 - # 负责跟踪用户消息活动、管理消息段、清理过期数据 - # ================================ - - def _update_message_segments(self, person_id: str, message_time: float): - """更新用户的消息段 - - Args: - person_id: 用户ID - message_time: 消息时间戳 - """ - if person_id not in self.person_engaged_cache: - self.person_engaged_cache[person_id] = [] - - segments = self.person_engaged_cache[person_id] - current_time = time.time() - - # 获取该消息前5条消息的时间作为潜在的开始时间 - before_messages = get_raw_msg_before_timestamp_with_chat(self.subheartflow_id, message_time, limit=5) - if before_messages: - # 由于get_raw_msg_before_timestamp_with_chat返回按时间升序排序的消息，最后一个是最接近message_time的 - # 我们需要第一个消息作为开始时间，但应该确保至少包含5条消息或该用户之前的消息 - potential_start_time = before_messages[0]["time"] - else: - # 如果没有前面的消息，就从当前消息开始 - potential_start_time = message_time - - # 如果没有现有消息段，创建新的 - if not segments: - new_segment = { - "start_time": potential_start_time, - "end_time": message_time, - "last_msg_time": message_time, - "message_count": self._count_messages_in_timerange(potential_start_time, message_time), - } - segments.append(new_segment) - - person_name = get_person_info_manager().get_value_sync(person_id, "person_name") or person_id - logger.info( - f"{self.log_prefix} 眼熟用户 {person_name} 在 {time.strftime('%H:%M:%S', time.localtime(potential_start_time))} - {time.strftime('%H:%M:%S', time.localtime(message_time))} 之间有 {new_segment['message_count']} 条消息" - ) - self._save_cache() - return - - # 获取最后一个消息段 - last_segment = segments[-1] - - # 计算从最后一条消息到当前消息之间的消息数量（不包含边界） - messages_between = self._count_messages_between(last_segment["last_msg_time"], message_time) - - if messages_between <= 10: - # 在10条消息内，延伸当前消息段 - last_segment["end_time"] = message_time - last_segment["last_msg_time"] = message_time - # 重新计算整个消息段的消息数量 - last_segment["message_count"] = self._count_messages_in_timerange( - last_segment["start_time"], last_segment["end_time"] - ) - logger.debug(f"{self.log_prefix} 延伸用户 {person_id} 的消息段: {last_segment}") - else: - # 超过10条消息，结束当前消息段并创建新的 - # 结束当前消息段：延伸到原消息段最后一条消息后5条消息的时间 - after_messages = get_raw_msg_by_timestamp_with_chat( - self.subheartflow_id, last_segment["last_msg_time"], current_time, limit=5, limit_mode="earliest" - ) - if after_messages and len(after_messages) >= 5: - # 如果有足够的后续消息，使用第5条消息的时间作为结束时间 - last_segment["end_time"] = after_messages[4]["time"] - else: - # 如果没有足够的后续消息，保持原有的结束时间 - pass - - # 重新计算当前消息段的消息数量 - last_segment["message_count"] = self._count_messages_in_timerange( - last_segment["start_time"], last_segment["end_time"] - ) - - # 创建新的消息段 - new_segment = { - "start_time": potential_start_time, - "end_time": message_time, - "last_msg_time": message_time, - "message_count": self._count_messages_in_timerange(potential_start_time, message_time), - } - segments.append(new_segment) - person_info_manager = get_person_info_manager() - person_name = person_info_manager.get_value_sync(person_id, "person_name") or person_id - logger.info(f"{self.log_prefix} 重新眼熟用户 {person_name} 创建新消息段（超过10条消息间隔）: {new_segment}") - - self._save_cache() - - def _count_messages_in_timerange(self, start_time: float, end_time: float) -> int: - """计算指定时间范围内的消息数量（包含边界）""" - messages = get_raw_msg_by_timestamp_with_chat_inclusive(self.subheartflow_id, start_time, end_time) - return len(messages) - - def _count_messages_between(self, start_time: float, end_time: float) -> int: - """计算两个时间点之间的消息数量（不包含边界），用于间隔检查""" - return num_new_messages_since(self.subheartflow_id, start_time, end_time) - - def _get_total_message_count(self, person_id: str) -> int: - """获取用户所有消息段的总消息数量""" - if person_id not in self.person_engaged_cache: - return 0 - - total_count = 0 - for segment in self.person_engaged_cache[person_id]: - total_count += segment["message_count"] - - return total_count - - def _cleanup_old_segments(self) -> bool: - """清理老旧的消息段 - - Returns: - bool: 是否执行了清理操作 - """ - if not SEGMENT_CLEANUP_CONFIG["enable_cleanup"]: - return False - - current_time = time.time() - - # 检查是否需要执行清理（基于时间间隔） - cleanup_interval_seconds = SEGMENT_CLEANUP_CONFIG["cleanup_interval_hours"] * 3600 - if current_time - self.last_cleanup_time < cleanup_interval_seconds: - return False - - logger.info(f"{self.log_prefix} 开始执行老消息段清理...") - - cleanup_stats = { - "users_cleaned": 0, - "segments_removed": 0, - "total_segments_before": 0, - "total_segments_after": 0, - } - - max_age_seconds = SEGMENT_CLEANUP_CONFIG["max_segment_age_days"] * 24 * 3600 - max_segments_per_user = SEGMENT_CLEANUP_CONFIG["max_segments_per_user"] - - users_to_remove = [] - - for person_id, segments in self.person_engaged_cache.items(): - cleanup_stats["total_segments_before"] += len(segments) - original_segment_count = len(segments) - - # 1. 按时间清理：移除过期的消息段 - segments_after_age_cleanup = [] - for segment in segments: - segment_age = current_time - segment["end_time"] - if segment_age <= max_age_seconds: - segments_after_age_cleanup.append(segment) - else: - cleanup_stats["segments_removed"] += 1 - logger.debug( - f"{self.log_prefix} 移除用户 {person_id} 的过期消息段: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(segment['start_time']))} - {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(segment['end_time']))}" - ) - - # 2. 按数量清理：如果消息段数量仍然过多，保留最新的 - if len(segments_after_age_cleanup) > max_segments_per_user: - # 按end_time排序，保留最新的 - segments_after_age_cleanup.sort(key=lambda x: x["end_time"], reverse=True) - segments_removed_count = len(segments_after_age_cleanup) - max_segments_per_user - cleanup_stats["segments_removed"] += segments_removed_count - segments_after_age_cleanup = segments_after_age_cleanup[:max_segments_per_user] - logger.debug( - f"{self.log_prefix} 用户 {person_id} 消息段数量过多，移除 {segments_removed_count} 个最老的消息段" - ) - - # 使用清理后的消息段 - - # 更新缓存 - if len(segments_after_age_cleanup) == 0: - # 如果没有剩余消息段，标记用户为待移除 - users_to_remove.append(person_id) - else: - self.person_engaged_cache[person_id] = segments_after_age_cleanup - cleanup_stats["total_segments_after"] += len(segments_after_age_cleanup) - - if original_segment_count != len(segments_after_age_cleanup): - cleanup_stats["users_cleaned"] += 1 - - # 移除没有消息段的用户 - for person_id in users_to_remove: - del self.person_engaged_cache[person_id] - logger.debug(f"{self.log_prefix} 移除用户 {person_id}：没有剩余消息段") - - # 更新最后清理时间 - self.last_cleanup_time = current_time - - # 保存缓存 - if cleanup_stats["segments_removed"] > 0 or len(users_to_remove) > 0: - self._save_cache() - logger.info( - f"{self.log_prefix} 清理完成 - 影响用户: {cleanup_stats['users_cleaned']}, 移除消息段: {cleanup_stats['segments_removed']}, 移除用户: {len(users_to_remove)}" - ) - logger.info( - f"{self.log_prefix} 消息段统计 - 清理前: {cleanup_stats['total_segments_before']}, 清理后: {cleanup_stats['total_segments_after']}" - ) - else: - logger.debug(f"{self.log_prefix} 清理完成 - 无需清理任何内容") - - return cleanup_stats["segments_removed"] > 0 or len(users_to_remove) > 0 - - def force_cleanup_user_segments(self, person_id: str) -> bool: - """强制清理指定用户的所有消息段 - - Args: - person_id: 用户ID - - Returns: - bool: 是否成功清理 - """ - if person_id in self.person_engaged_cache: - segments_count = len(self.person_engaged_cache[person_id]) - del self.person_engaged_cache[person_id] - self._save_cache() - logger.info(f"{self.log_prefix} 强制清理用户 {person_id} 的 {segments_count} 个消息段") - return True - return False - - def get_cache_status(self) -> str: - """获取缓存状态信息，用于调试和监控""" - if not self.person_engaged_cache: - return f"{self.log_prefix} 关系缓存为空" - - status_lines = [f"{self.log_prefix} 关系缓存状态："] - status_lines.append( - f"最后处理消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.last_processed_message_time)) if self.last_processed_message_time > 0 else '未设置'}" - ) - status_lines.append( - f"最后清理时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.last_cleanup_time)) if self.last_cleanup_time > 0 else '未执行'}" - ) - status_lines.append(f"总用户数：{len(self.person_engaged_cache)}") - status_lines.append( - f"清理配置：{'启用' if SEGMENT_CLEANUP_CONFIG['enable_cleanup'] else '禁用'} (最大保存{SEGMENT_CLEANUP_CONFIG['max_segment_age_days']}天, 每用户最多{SEGMENT_CLEANUP_CONFIG['max_segments_per_user']}段)" - ) - status_lines.append("") - - for person_id, segments in self.person_engaged_cache.items(): - total_count = self._get_total_message_count(person_id) - status_lines.append(f"用户 {person_id}:") - status_lines.append(f" 总消息数：{total_count} ({total_count}/45)") - status_lines.append(f" 消息段数：{len(segments)}") - - for i, segment in enumerate(segments): - start_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(segment["start_time"])) - end_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(segment["end_time"])) - last_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(segment["last_msg_time"])) - status_lines.append( - f" 段{i + 1}: {start_str} -> {end_str} (最后消息: {last_str}, 消息数: {segment['message_count']})" - ) - status_lines.append("") - - return "\n".join(status_lines) - - # ================================ - # 主要处理流程 - # 统筹各模块协作、对外提供服务接口 - # ================================ - - async def process_info( - self, - observations: List[Observation] = None, - action_type: str = None, - action_data: dict = None, - **kwargs, - ) -> List[InfoBase]: - """处理信息对象 - - Args: - observations: 观察对象列表 - action_type: 动作类型 - action_data: 动作数据 - - Returns: - List[InfoBase]: 处理后的结构化信息列表 - """ - await self.build_relation(observations) - - relation_info_str = await self.relation_identify(observations, action_type, action_data) - - if relation_info_str: - relation_info = RelationInfo() - relation_info.set_relation_info(relation_info_str) - else: - relation_info = None - return None - - return [relation_info] - - async def build_relation(self, observations: List[Observation] = None): - """构建关系""" - self._cleanup_old_segments() - current_time = time.time() - - if observations: - for observation in observations: - if isinstance(observation, ChattingObservation): - latest_messages = get_raw_msg_by_timestamp_with_chat( - self.subheartflow_id, - self.last_processed_message_time, - current_time, - limit=50, # 获取自上次处理后的消息 - ) - if latest_messages: - # 处理所有新的非bot消息 - for latest_msg in latest_messages: - user_id = latest_msg.get("user_id") - platform = latest_msg.get("user_platform") or latest_msg.get("chat_info_platform") - msg_time = latest_msg.get("time", 0) - - if ( - user_id - and platform - and user_id != global_config.bot.qq_account - and msg_time > self.last_processed_message_time - ): - from src.person_info.person_info import PersonInfoManager - - person_id = PersonInfoManager.get_person_id(platform, user_id) - self._update_message_segments(person_id, msg_time) - logger.debug( - f"{self.log_prefix} 更新用户 {person_id} 的消息段，消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(msg_time))}" - ) - self.last_processed_message_time = max(self.last_processed_message_time, msg_time) - break - - # 1. 检查是否有用户达到关系构建条件（总消息数达到45条） - users_to_build_relationship = [] - for person_id, segments in self.person_engaged_cache.items(): - total_message_count = self._get_total_message_count(person_id) - if total_message_count >= 45: - users_to_build_relationship.append(person_id) - logger.info( - f"{self.log_prefix} 用户 {person_id} 满足关系构建条件，总消息数：{total_message_count}，消息段数：{len(segments)}" - ) - elif total_message_count > 0: - # 记录进度信息 - logger.debug( - f"{self.log_prefix} 用户 {person_id} 进度：{total_message_count}/45 条消息，{len(segments)} 个消息段" - ) - - # 2. 为满足条件的用户构建关系 - for person_id in users_to_build_relationship: - segments = self.person_engaged_cache[person_id] - # 异步执行关系构建 - asyncio.create_task(self.update_impression_on_segments(person_id, self.subheartflow_id, segments)) - # 移除已处理的用户缓存 - del self.person_engaged_cache[person_id] - self._save_cache() - - async def relation_identify( - self, - observations: List[Observation] = None, - action_type: str = None, - action_data: dict = None, - ): - """ - 从人物获取信息 - """ - - chat_observe_info = "" - current_time = time.time() - if observations: - for observation in observations: - if isinstance(observation, ChattingObservation): - chat_observe_info = observation.get_observe_info() - # latest_message_time = observation.last_observe_time - # 从聊天观察中提取用户信息并更新消息段 - # 获取最新的非bot消息来更新消息段 - latest_messages = get_raw_msg_by_timestamp_with_chat( - self.subheartflow_id, - self.last_processed_message_time, - current_time, - limit=50, # 获取自上次处理后的消息 - ) - if latest_messages: - # 处理所有新的非bot消息 - for latest_msg in latest_messages: - user_id = latest_msg.get("user_id") - platform = latest_msg.get("user_platform") or latest_msg.get("chat_info_platform") - msg_time = latest_msg.get("time", 0) - - if ( - user_id - and platform - and user_id != global_config.bot.qq_account - and msg_time > self.last_processed_message_time - ): - from src.person_info.person_info import PersonInfoManager - - person_id = PersonInfoManager.get_person_id(platform, user_id) - self._update_message_segments(person_id, msg_time) - logger.debug( - f"{self.log_prefix} 更新用户 {person_id} 的消息段，消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(msg_time))}" - ) - self.last_processed_message_time = max(self.last_processed_message_time, msg_time) - break - - for person_id in list(self.info_fetched_cache.keys()): - for info_type in list(self.info_fetched_cache[person_id].keys()): - self.info_fetched_cache[person_id][info_type]["ttl"] -= 1 - if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0: - del self.info_fetched_cache[person_id][info_type] - if not self.info_fetched_cache[person_id]: - del self.info_fetched_cache[person_id] - - if action_type != "reply": - return None - - target_message = action_data.get("reply_to", "") - - if ":" in target_message: - parts = target_message.split(":", 1) - elif "：" in target_message: - parts = target_message.split("：", 1) - else: - logger.warning(f"reply_to格式不正确: {target_message}，跳过关系识别") - return None - - if len(parts) != 2: - logger.warning(f"reply_to格式不正确: {target_message}，跳过关系识别") - return None - - sender = parts[0].strip() - text = parts[1].strip() - - person_info_manager = get_person_info_manager() - person_id = person_info_manager.get_person_id_by_person_name(sender) - - if not person_id: - logger.warning(f"未找到用户 {sender} 的ID，跳过关系识别") - return None - - nickname_str = ",".join(global_config.bot.alias_names) - name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" - - info_cache_block = "" - if self.info_fetching_cache: - # 对于每个(person_id, info_type)组合，只保留最新的记录 - latest_records = {} - for info_fetching in self.info_fetching_cache: - key = (info_fetching["person_id"], info_fetching["info_type"]) - if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]: - latest_records[key] = info_fetching - - # 按时间排序并生成显示文本 - sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"]) - for info_fetching in sorted_records: - info_cache_block += ( - f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n" - ) - - prompt = (await global_prompt_manager.get_prompt_async("relationship_prompt")).format( - chat_observe_info=chat_observe_info, - name_block=name_block, - info_cache_block=info_cache_block, - person_name=sender, - target_message=text, - ) - - try: - logger.info(f"{self.log_prefix} 人物信息prompt: \n{prompt}\n") - content, _ = await self.llm_model.generate_response_async(prompt=prompt) - if content: - # print(f"content: {content}") - content_json = json.loads(repair_json(content)) - - # 检查是否返回了不需要查询的标志 - if "none" in content_json: - logger.info(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}") - # 跳过新的信息提取，但仍会处理已有缓存 - else: - info_type = content_json.get("info_type") - if info_type: - self.info_fetching_cache.append( - { - "person_id": person_id, - "person_name": sender, - "info_type": info_type, - "start_time": time.time(), - "forget": False, - } - ) - if len(self.info_fetching_cache) > 20: - self.info_fetching_cache.pop(0) - - logger.info(f"{self.log_prefix} 调取用户 {sender} 的[{info_type}]信息。") - - # 执行信息提取 - await self._fetch_single_info_instant(person_id, info_type, time.time()) - else: - logger.warning(f"{self.log_prefix} LLM did not return a valid info_type. Response: {content}") - - except Exception as e: - logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}") - logger.error(traceback.format_exc()) - - # 7. 合并缓存和新处理的信息 - persons_infos_str = "" - # 处理已获取到的信息 - if self.info_fetched_cache: - persons_with_known_info = [] # 有已知信息的人员 - persons_with_unknown_info = [] # 有未知信息的人员 - - for person_id in self.info_fetched_cache: - person_known_infos = [] - person_unknown_infos = [] - person_name = "" - - for info_type in self.info_fetched_cache[person_id]: - person_name = self.info_fetched_cache[person_id][info_type]["person_name"] - if not self.info_fetched_cache[person_id][info_type]["unknow"]: - info_content = self.info_fetched_cache[person_id][info_type]["info"] - person_known_infos.append(f"[{info_type}]：{info_content}") - else: - person_unknown_infos.append(info_type) - - # 如果有已知信息，添加到已知信息列表 - if person_known_infos: - known_info_str = "；".join(person_known_infos) + "；" - persons_with_known_info.append((person_name, known_info_str)) - - # 如果有未知信息，添加到未知信息列表 - if person_unknown_infos: - persons_with_unknown_info.append((person_name, person_unknown_infos)) - - # 先输出有已知信息的人员 - for person_name, known_info_str in persons_with_known_info: - persons_infos_str += f"你对 {person_name} 的了解：{known_info_str}\n" - - # 统一处理未知信息，避免重复的警告文本 - if persons_with_unknown_info: - unknown_persons_details = [] - for person_name, unknown_types in persons_with_unknown_info: - unknown_types_str = "、".join(unknown_types) - unknown_persons_details.append(f"{person_name}的[{unknown_types_str}]") - - if len(unknown_persons_details) == 1: - persons_infos_str += ( - f"你不了解{unknown_persons_details[0]}信息，不要胡乱回答，可以直接说不知道或忘记了；\n" - ) - else: - unknown_all_str = "、".join(unknown_persons_details) - persons_infos_str += f"你不了解{unknown_all_str}等信息，不要胡乱回答，可以直接说不知道或忘记了；\n" - - return persons_infos_str - - # ================================ - # 关系构建模块 - # 负责触发关系构建、整合消息段、更新用户印象 - # ================================ - - async def update_impression_on_segments(self, person_id: str, chat_id: str, segments: List[Dict[str, any]]): - """ - 基于消息段更新用户印象 - - Args: - person_id: 用户ID - chat_id: 聊天ID - segments: 消息段列表 - """ - logger.debug(f"开始为 {person_id} 基于 {len(segments)} 个消息段更新印象") - try: - processed_messages = [] - - for i, segment in enumerate(segments): - start_time = segment["start_time"] - end_time = segment["end_time"] - segment["message_count"] - start_date = time.strftime("%Y-%m-%d %H:%M", time.localtime(start_time)) - - # 获取该段的消息（包含边界） - segment_messages = get_raw_msg_by_timestamp_with_chat_inclusive( - self.subheartflow_id, start_time, end_time - ) - logger.info( - f"消息段 {i + 1}: {start_date} - {time.strftime('%Y-%m-%d %H:%M', time.localtime(end_time))}, 消息数: {len(segment_messages)}" - ) - - if segment_messages: - # 如果不是第一个消息段，在消息列表前添加间隔标识 - if i > 0: - # 创建一个特殊的间隔消息 - gap_message = { - "time": start_time - 0.1, # 稍微早于段开始时间 - "user_id": "system", - "user_platform": "system", - "user_nickname": "系统", - "user_cardname": "", - "display_message": f"...（中间省略一些消息）{start_date} 之后的消息如下...", - "is_action_record": True, - "chat_info_platform": segment_messages[0].get("chat_info_platform", ""), - "chat_id": chat_id, - } - processed_messages.append(gap_message) - - # 添加该段的所有消息 - processed_messages.extend(segment_messages) - - if processed_messages: - # 按时间排序所有消息（包括间隔标识） - processed_messages.sort(key=lambda x: x["time"]) - - logger.info(f"为 {person_id} 获取到总共 {len(processed_messages)} 条消息（包含间隔标识）用于印象更新") - relationship_manager = get_relationship_manager() - - # 调用原有的更新方法 - await relationship_manager.update_person_impression( - person_id=person_id, timestamp=time.time(), bot_engaged_messages=processed_messages - ) - else: - logger.info(f"没有找到 {person_id} 的消息段对应的消息，不更新印象") - - except Exception as e: - logger.error(f"为 {person_id} 更新印象时发生错误: {e}") - logger.error(traceback.format_exc()) - - # ================================ - # 信息调取模块 - # 负责实时分析对话需求、提取用户信息、管理信息缓存 - # ================================ - - async def _fetch_single_info_instant(self, person_id: str, info_type: str, start_time: float): - """ - 使用小模型提取单个信息类型 - """ - person_info_manager = get_person_info_manager() - - # 首先检查 info_list 缓存 - info_list = await person_info_manager.get_value(person_id, "info_list") or [] - cached_info = None - person_name = await person_info_manager.get_value(person_id, "person_name") - - # print(f"info_list: {info_list}") - - # 查找对应的 info_type - for info_item in info_list: - if info_item.get("info_type") == info_type: - cached_info = info_item.get("info_content") - logger.debug(f"{self.log_prefix} 在info_list中找到 {person_name} 的 {info_type} 信息: {cached_info}") - break - - # 如果缓存中有信息，直接使用 - if cached_info: - if person_id not in self.info_fetched_cache: - self.info_fetched_cache[person_id] = {} - - self.info_fetched_cache[person_id][info_type] = { - "info": cached_info, - "ttl": 2, - "start_time": start_time, - "person_name": person_name, - "unknow": cached_info == "none", - } - logger.info(f"{self.log_prefix} 记得 {person_name} 的 {info_type}: {cached_info}") - return - - try: - person_name = await person_info_manager.get_value(person_id, "person_name") - person_impression = await person_info_manager.get_value(person_id, "impression") - if person_impression: - person_impression_block = ( - f"<对{person_name}的总体了解>\n{person_impression}\n" - ) - else: - person_impression_block = "" - - points = await person_info_manager.get_value(person_id, "points") - if points: - points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points]) - points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n" - else: - points_text_block = "" - - if not points_text_block and not person_impression_block: - if person_id not in self.info_fetched_cache: - self.info_fetched_cache[person_id] = {} - self.info_fetched_cache[person_id][info_type] = { - "info": "none", - "ttl": 2, - "start_time": start_time, - "person_name": person_name, - "unknow": True, - } - logger.info(f"{self.log_prefix} 完全不认识 {person_name}") - await self._save_info_to_cache(person_id, info_type, "none") - return - - nickname_str = ",".join(global_config.bot.alias_names) - name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" - prompt = (await global_prompt_manager.get_prompt_async("fetch_person_info_prompt")).format( - name_block=name_block, - info_type=info_type, - person_impression_block=person_impression_block, - person_name=person_name, - info_json_str=f'"{info_type}": "有关{info_type}的信息内容"', - points_text_block=points_text_block, - ) - except Exception: - logger.error(traceback.format_exc()) - return - - try: - # 使用小模型进行即时提取 - content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt) - - if content: - content_json = json.loads(repair_json(content)) - if info_type in content_json: - info_content = content_json[info_type] - is_unknown = info_content == "none" or not info_content - - # 保存到运行时缓存 - if person_id not in self.info_fetched_cache: - self.info_fetched_cache[person_id] = {} - self.info_fetched_cache[person_id][info_type] = { - "info": "unknow" if is_unknown else info_content, - "ttl": 3, - "start_time": start_time, - "person_name": person_name, - "unknow": is_unknown, - } - - # 保存到持久化缓存 (info_list) - await self._save_info_to_cache(person_id, info_type, info_content if not is_unknown else "none") - - if not is_unknown: - logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {content}") - else: - logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息") - else: - logger.warning(f"{self.log_prefix} 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。") - except Exception as e: - logger.error(f"{self.log_prefix} 执行小模型请求获取用户信息时出错: {e}") - logger.error(traceback.format_exc()) - - async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str): - """ - 将提取到的信息保存到 person_info 的 info_list 字段中 - - Args: - person_id: 用户ID - info_type: 信息类型 - info_content: 信息内容 - """ - try: - person_info_manager = get_person_info_manager() - - # 获取现有的 info_list - info_list = await person_info_manager.get_value(person_id, "info_list") or [] - - # 查找是否已存在相同 info_type 的记录 - found_index = -1 - for i, info_item in enumerate(info_list): - if isinstance(info_item, dict) and info_item.get("info_type") == info_type: - found_index = i - break - - # 创建新的信息记录 - new_info_item = { - "info_type": info_type, - "info_content": info_content, - } - - if found_index >= 0: - # 更新现有记录 - info_list[found_index] = new_info_item - logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存") - else: - # 添加新记录 - info_list.append(new_info_item) - logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存") - - # 保存更新后的 info_list - await person_info_manager.update_one_field(person_id, "info_list", info_list) - - except Exception as e: - logger.error(f"{self.log_prefix} [缓存保存] 保存信息到缓存失败: {e}") - logger.error(traceback.format_exc()) - - -init_prompt() diff --git a/src/chat/focus_chat/info_processors/tool_processor.py b/src/chat/focus_chat/info_processors/tool_processor.py deleted file mode 100644 index f0034af1d..000000000 --- a/src/chat/focus_chat/info_processors/tool_processor.py +++ /dev/null @@ -1,186 +0,0 @@ -from src.chat.heart_flow.observation.chatting_observation import ChattingObservation -from src.llm_models.utils_model import LLMRequest -from src.config.config import global_config -import time -from src.common.logger import get_logger -from src.individuality.individuality import get_individuality -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.tools.tool_use import ToolUser -from src.chat.utils.json_utils import process_llm_tool_calls -from .base_processor import BaseProcessor -from typing import List -from src.chat.heart_flow.observation.observation import Observation -from src.chat.focus_chat.info.structured_info import StructuredInfo -from src.chat.heart_flow.observation.structure_observation import StructureObservation - -logger = get_logger("processor") - - -def init_prompt(): - # ... 原有代码 ... - - # 添加工具执行器提示词 - tool_executor_prompt = """ -你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。 -群里正在进行的聊天内容： -{chat_observe_info} - -请仔细分析聊天内容，考虑以下几点： -1. 内容中是否包含需要查询信息的问题 -2. 是否有明确的工具使用指令 - -If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed". -""" - Prompt(tool_executor_prompt, "tool_executor_prompt") - - -class ToolProcessor(BaseProcessor): - log_prefix = "工具执行器" - - def __init__(self, subheartflow_id: str): - super().__init__() - self.subheartflow_id = subheartflow_id - self.log_prefix = f"[{subheartflow_id}:ToolExecutor] " - self.llm_model = LLMRequest( - model=global_config.model.focus_tool_use, - request_type="focus.processor.tool", - ) - self.structured_info = [] - - async def process_info( - self, - observations: List[Observation] = None, - action_type: str = None, - action_data: dict = None, - **kwargs, - ) -> List[StructuredInfo]: - """处理信息对象 - - Args: - observations: 可选的观察列表，包含ChattingObservation和StructureObservation类型 - action_type: 动作类型 - action_data: 动作数据 - **kwargs: 其他可选参数 - - Returns: - list: 处理后的结构化信息列表 - """ - - working_infos = [] - result = [] - - if observations: - for observation in observations: - if isinstance(observation, ChattingObservation): - result, used_tools, prompt = await self.execute_tools(observation) - - logger.info(f"工具调用结果: {result}") - # 更新WorkingObservation中的结构化信息 - for observation in observations: - if isinstance(observation, StructureObservation): - for structured_info in result: - # logger.debug(f"{self.log_prefix} 更新WorkingObservation中的结构化信息: {structured_info}") - observation.add_structured_info(structured_info) - - working_infos = observation.get_observe_info() - logger.debug(f"{self.log_prefix} 获取更新后WorkingObservation中的结构化信息: {working_infos}") - - structured_info = StructuredInfo() - if working_infos: - for working_info in working_infos: - structured_info.set_info(key=working_info.get("type"), value=working_info.get("content")) - - return [structured_info] - - async def execute_tools(self, observation: ChattingObservation, action_type: str = None, action_data: dict = None): - """ - 并行执行工具，返回结构化信息 - - 参数: - sub_mind: 子思维对象 - chat_target_name: 聊天目标名称，默认为"对方" - is_group_chat: 是否为群聊，默认为False - return_details: 是否返回详细信息，默认为False - cycle_info: 循环信息对象，可用于记录详细执行信息 - action_type: 动作类型 - action_data: 动作数据 - - 返回: - 如果return_details为False: - List[Dict]: 工具执行结果的结构化信息列表 - 如果return_details为True: - Tuple[List[Dict], List[str], str]: (工具执行结果列表, 使用的工具列表, 工具执行提示词) - """ - tool_instance = ToolUser() - tools = tool_instance._define_tools() - - # logger.debug(f"observation: {observation}") - # logger.debug(f"observation.chat_target_info: {observation.chat_target_info}") - # logger.debug(f"observation.is_group_chat: {observation.is_group_chat}") - # logger.debug(f"observation.person_list: {observation.person_list}") - - is_group_chat = observation.is_group_chat - - # chat_observe_info = observation.get_observe_info() - chat_observe_info = observation.talking_message_str_truncate_short - # person_list = observation.person_list - - # 获取时间信息 - time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) - - # 构建专用于工具调用的提示词 - prompt = await global_prompt_manager.format_prompt( - "tool_executor_prompt", - chat_observe_info=chat_observe_info, - is_group_chat=is_group_chat, - bot_name=get_individuality().name, - time_now=time_now, - ) - - # 调用LLM，专注于工具使用 - # logger.info(f"开始执行工具调用{prompt}") - response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools) - - if len(other_info) == 3: - reasoning_content, model_name, tool_calls = other_info - else: - reasoning_content, model_name = other_info - tool_calls = None - - # print("tooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltool") - if tool_calls: - logger.info(f"获取到工具原始输出:\n{tool_calls}") - # 处理工具调用和结果收集，类似于SubMind中的逻辑 - new_structured_items = [] - used_tools = [] # 记录使用了哪些工具 - - if tool_calls: - success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls) - if success and valid_tool_calls: - for tool_call in valid_tool_calls: - try: - # 记录使用的工具名称 - tool_name = tool_call.get("name", "unknown_tool") - used_tools.append(tool_name) - - result = await tool_instance._execute_tool_call(tool_call) - - name = result.get("type", "unknown_type") - content = result.get("content", "") - - logger.info(f"工具{name}，获得信息:{content}") - if result: - new_item = { - "type": result.get("type", "unknown_type"), - "id": result.get("id", f"tool_exec_{time.time()}"), - "content": result.get("content", ""), - "ttl": 3, - } - new_structured_items.append(new_item) - except Exception as e: - logger.error(f"{self.log_prefix}工具执行失败: {e}") - - return new_structured_items, used_tools, prompt - - -init_prompt() diff --git a/src/chat/focus_chat/memory_activator.py b/src/chat/focus_chat/memory_activator.py index fb92c0024..bfe6a58e5 100644 --- a/src/chat/focus_chat/memory_activator.py +++ b/src/chat/focus_chat/memory_activator.py @@ -1,5 +1,3 @@ -from src.chat.heart_flow.observation.chatting_observation import ChattingObservation -from src.chat.heart_flow.observation.structure_observation import StructureObservation from src.llm_models.utils_model import LLMRequest from src.config.config import global_config from src.common.logger import get_logger @@ -48,9 +46,12 @@ def init_prompt(): # --- Group Chat Prompt --- memory_activator_prompt = """ 你是一个记忆分析器，你需要根据以下信息来进行回忆 - 以下是一场聊天中的信息，请根据这些信息，总结出几个关键词作为记忆回忆的触发词 + 以下是一段聊天记录，请根据这些信息，总结出几个关键词作为记忆回忆的触发词 + 聊天记录: {obs_info_text} + 你想要回复的消息: + {target_message} 历史关键词（请避免重复提取这些关键词）： {cached_keywords} @@ -71,12 +72,12 @@ class MemoryActivator: self.summary_model = LLMRequest( model=global_config.model.memory_summary, temperature=0.7, - request_type="focus.memory_activator", + request_type="memory_activator", ) self.running_memory = [] self.cached_keywords = set() # 用于缓存历史关键词 - async def activate_memory(self, observations) -> List[Dict]: + async def activate_memory_with_chat_history(self, target_message, chat_history_prompt) -> List[Dict]: """ 激活记忆 @@ -90,23 +91,13 @@ class MemoryActivator: if not global_config.memory.enable_memory: return [] - obs_info_text = "" - for observation in observations: - if isinstance(observation, ChattingObservation): - obs_info_text += observation.talking_message_str_truncate_short - elif isinstance(observation, StructureObservation): - working_info = observation.get_observe_info() - for working_info_item in working_info: - obs_info_text += f"{working_info_item['type']}: {working_info_item['content']}\n" - - # logger.info(f"回忆待检索内容：obs_info_text: {obs_info_text}") - # 将缓存的关键词转换为字符串，用于prompt cached_keywords_str = ", ".join(self.cached_keywords) if self.cached_keywords else "暂无历史关键词" prompt = await global_prompt_manager.format_prompt( "memory_activator_prompt", - obs_info_text=obs_info_text, + obs_info_text=chat_history_prompt, + target_message=target_message, cached_keywords=cached_keywords_str, ) @@ -132,9 +123,6 @@ class MemoryActivator: related_memory = await hippocampus_manager.get_memory_from_topic( valid_keywords=keywords, max_memory_num=3, max_memory_length=2, max_depth=3 ) - # related_memory = await hippocampus_manager.get_memory_from_text( - # text=obs_info_text, max_memory_num=5, max_memory_length=2, max_depth=3, fast_retrieval=False - # ) logger.info(f"获取到的记忆: {related_memory}") diff --git a/src/chat/focus_chat/planners/planner_simple.py b/src/chat/focus_chat/planners/planner_simple.py index e891a9769..20f41c711 100644 --- a/src/chat/focus_chat/planners/planner_simple.py +++ b/src/chat/focus_chat/planners/planner_simple.py @@ -236,14 +236,6 @@ class ActionPlanner(BasePlanner): action_data["loop_start_time"] = loop_start_time - memory_str = "" - if running_memorys: - memory_str = "以下是当前在聊天中，你回忆起的记忆：\n" - for running_memory in running_memorys: - memory_str += f"{running_memory['content']}\n" - if memory_str: - action_data["memory_block"] = memory_str - # 对于reply动作不需要额外处理，因为相关字段已经在上面的循环中添加到action_data if extracted_action not in current_available_actions: diff --git a/src/chat/heart_flow/observation/chatting_observation.py b/src/chat/heart_flow/observation/chatting_observation.py index 8888ddb43..1a41ede1f 100644 --- a/src/chat/heart_flow/observation/chatting_observation.py +++ b/src/chat/heart_flow/observation/chatting_observation.py @@ -8,14 +8,9 @@ from src.chat.utils.chat_message_builder import ( get_person_id_list, ) from src.chat.utils.prompt_builder import global_prompt_manager, Prompt -from typing import Optional -import difflib -from src.chat.message_receive.message import MessageRecv from src.chat.heart_flow.observation.observation import Observation from src.common.logger import get_logger from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info -from src.chat.message_receive.chat_stream import get_chat_manager -from src.person_info.person_info import get_person_info_manager logger = get_logger("observation") @@ -67,7 +62,7 @@ class ChattingObservation(Observation): self.talking_message_str_truncate_short = "" self.name = global_config.bot.nickname self.nick_name = global_config.bot.alias_names - self.max_now_obs_len = global_config.focus_chat.observation_context_size + self.max_now_obs_len = global_config.chat.max_context_size self.overlap_len = global_config.focus_chat.compressed_length self.person_list = [] self.compressor_prompt = "" @@ -108,75 +103,6 @@ class ChattingObservation(Observation): def get_observe_info(self, ids=None): return self.talking_message_str - def get_recv_message_by_text(self, sender: str, text: str) -> Optional[MessageRecv]: - """ - 根据回复的纯文本 - 1. 在talking_message中查找最新的，最匹配的消息 - 2. 如果找到，则返回消息 - """ - find_msg = None - reverse_talking_message = list(reversed(self.talking_message)) - - for message in reverse_talking_message: - user_id = message["user_id"] - platform = message["platform"] - person_id = get_person_info_manager().get_person_id(platform, user_id) - person_name = get_person_info_manager().get_value(person_id, "person_name") - if person_name == sender: - similarity = difflib.SequenceMatcher(None, text, message["processed_plain_text"]).ratio() - if similarity >= 0.9: - find_msg = message - break - - if not find_msg: - return None - - user_info = { - "platform": find_msg.get("user_platform", ""), - "user_id": find_msg.get("user_id", ""), - "user_nickname": find_msg.get("user_nickname", ""), - "user_cardname": find_msg.get("user_cardname", ""), - } - - group_info = {} - if find_msg.get("chat_info_group_id"): - group_info = { - "platform": find_msg.get("chat_info_group_platform", ""), - "group_id": find_msg.get("chat_info_group_id", ""), - "group_name": find_msg.get("chat_info_group_name", ""), - } - - content_format = "" - accept_format = "" - template_items = {} - - format_info = {"content_format": content_format, "accept_format": accept_format} - template_info = { - "template_items": template_items, - } - - message_info = { - "platform": self.platform, - "message_id": find_msg.get("message_id"), - "time": find_msg.get("time"), - "group_info": group_info, - "user_info": user_info, - "additional_config": find_msg.get("additional_config"), - "format_info": format_info, - "template_info": template_info, - } - message_dict = { - "message_info": message_info, - "raw_message": find_msg.get("processed_plain_text"), - "detailed_plain_text": find_msg.get("processed_plain_text"), - "processed_plain_text": find_msg.get("processed_plain_text"), - } - find_rec_msg = MessageRecv(message_dict) - - find_rec_msg.update_chat_stream(get_chat_manager().get_or_create_stream(self.chat_id)) - - return find_rec_msg - async def observe(self): # 自上一次观察的新消息 new_messages_list = get_raw_msg_by_timestamp_with_chat( diff --git a/src/chat/heart_flow/observation/structure_observation.py b/src/chat/heart_flow/observation/structure_observation.py deleted file mode 100644 index f8ba27ba5..000000000 --- a/src/chat/heart_flow/observation/structure_observation.py +++ /dev/null @@ -1,42 +0,0 @@ -from datetime import datetime -from src.common.logger import get_logger - -# Import the new utility function - -logger = get_logger("observation") - - -# 所有观察的基类 -class StructureObservation: - def __init__(self, observe_id): - self.observe_info = "" - self.observe_id = observe_id - self.last_observe_time = datetime.now().timestamp() # 初始化为当前时间 - self.history_loop = [] - self.structured_info = [] - - def to_dict(self) -> dict: - """将观察对象转换为可序列化的字典""" - return { - "observe_info": self.observe_info, - "observe_id": self.observe_id, - "last_observe_time": self.last_observe_time, - "history_loop": self.history_loop, - "structured_info": self.structured_info, - } - - def get_observe_info(self): - return self.structured_info - - def add_structured_info(self, structured_info: dict): - self.structured_info.append(structured_info) - - async def observe(self): - observed_structured_infos = [] - for structured_info in self.structured_info: - if structured_info.get("ttl") > 0: - structured_info["ttl"] -= 1 - observed_structured_infos.append(structured_info) - logger.debug(f"观察到结构化信息仍旧在: {structured_info}") - - self.structured_info = observed_structured_infos diff --git a/src/chat/heart_flow/sub_heartflow.py b/src/chat/heart_flow/sub_heartflow.py index d602ea3a8..206c00364 100644 --- a/src/chat/heart_flow/sub_heartflow.py +++ b/src/chat/heart_flow/sub_heartflow.py @@ -62,7 +62,10 @@ class SubHeartflow: """异步初始化方法，创建兴趣流并确定聊天类型""" # 根据配置决定初始状态 - if global_config.chat.chat_mode == "focus": + if not self.is_group_chat: + logger.debug(f"{self.log_prefix} 检测到是私聊，将直接尝试进入 FOCUSED 状态。") + await self.change_chat_state(ChatState.FOCUSED) + elif global_config.chat.chat_mode == "focus": logger.debug(f"{self.log_prefix} 配置为 focus 模式，将直接尝试进入 FOCUSED 状态。") await self.change_chat_state(ChatState.FOCUSED) else: # "auto" 或其他模式保持原有逻辑或默认为 NORMAL @@ -123,6 +126,7 @@ class SubHeartflow: chat_stream=chat_stream, interest_dict=self.interest_dict, on_switch_to_focus_callback=self._handle_switch_to_focus_request, + get_cooldown_progress_callback=self.get_cooldown_progress, ) logger.info(f"{log_prefix} 开始普通聊天，随便水群...") @@ -134,27 +138,31 @@ class SubHeartflow: self.normal_chat_instance = None # 启动/初始化失败，清理实例 return False - async def _handle_switch_to_focus_request(self) -> None: + async def _handle_switch_to_focus_request(self) -> bool: """ 处理来自NormalChat的切换到focus模式的请求 Args: stream_id: 请求切换的stream_id + Returns: + bool: 切换成功返回True，失败返回False """ logger.info(f"{self.log_prefix} 收到NormalChat请求切换到focus模式") # 检查是否在focus冷却期内 if self.is_in_focus_cooldown(): logger.info(f"{self.log_prefix} 正在focus冷却期内，忽略切换到focus模式的请求") - return + return False # 切换到focus模式 current_state = self.chat_state.chat_status if current_state == ChatState.NORMAL: await self.change_chat_state(ChatState.FOCUSED) logger.info(f"{self.log_prefix} 已根据NormalChat请求从NORMAL切换到FOCUSED状态") + return True else: logger.warning(f"{self.log_prefix} 当前状态为{current_state.value}，无法切换到FOCUSED状态") + return False async def _handle_stop_focus_chat_request(self) -> None: """ @@ -360,17 +368,6 @@ class SubHeartflow: return self.normal_chat_instance.get_action_manager() return None - def set_normal_chat_planner_enabled(self, enabled: bool): - """设置NormalChat的planner是否启用 - - Args: - enabled: 是否启用planner - """ - if self.normal_chat_instance: - self.normal_chat_instance.set_planner_enabled(enabled) - else: - logger.warning(f"{self.log_prefix} NormalChat实例不存在，无法设置planner状态") - async def get_full_state(self) -> dict: """获取子心流的完整状态，包括兴趣、思维和聊天状态。""" return { @@ -436,3 +433,26 @@ class SubHeartflow: ) return is_cooling + + def get_cooldown_progress(self) -> float: + """获取冷却进度，返回0-1之间的值 + + Returns: + float: 0表示刚开始冷却，1表示冷却完成 + """ + if self.last_focus_exit_time == 0: + return 1.0 # 没有冷却，返回1表示完全恢复 + + # 基础冷却时间10分钟，受auto_focus_threshold调控 + base_cooldown = 10 * 60 # 10分钟转换为秒 + cooldown_duration = base_cooldown / global_config.chat.auto_focus_threshold + + current_time = time.time() + elapsed_since_exit = current_time - self.last_focus_exit_time + + if elapsed_since_exit >= cooldown_duration: + return 1.0 # 冷却完成 + + # 计算进度：0表示刚开始冷却，1表示冷却完成 + progress = elapsed_since_exit / cooldown_duration + return progress diff --git a/src/chat/heart_flow/subheartflow_manager.py b/src/chat/heart_flow/subheartflow_manager.py index faaac5ceb..587234cba 100644 --- a/src/chat/heart_flow/subheartflow_manager.py +++ b/src/chat/heart_flow/subheartflow_manager.py @@ -91,16 +91,10 @@ class SubHeartflowManager: return subflow try: - # 初始化子心流, 传入 mai_state_info new_subflow = SubHeartflow( subheartflow_id, ) - # 首先创建并添加聊天观察者 - # observation = ChattingObservation(chat_id=subheartflow_id) - # await observation.initialize() - # new_subflow.add_observation(observation) - # 然后再进行异步初始化，此时 SubHeartflow 内部若需启动 HeartFChatting，就能拿到 observation await new_subflow.initialize() diff --git a/src/chat/message_receive/bot.py b/src/chat/message_receive/bot.py index 62f074636..7227a929d 100644 --- a/src/chat/message_receive/bot.py +++ b/src/chat/message_receive/bot.py @@ -1,4 +1,5 @@ import traceback +import os from typing import Dict, Any from src.common.logger import get_logger @@ -13,13 +14,65 @@ from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.config.config import global_config from src.plugin_system.core.component_registry import component_registry # 导入新插件系统 from src.plugin_system.base.base_command import BaseCommand +from src.mais4u.mais4u_chat.s4u_msg_processor import S4UMessageProcessor +from maim_message import UserInfo +from src.chat.message_receive.chat_stream import ChatStream +import re # 定义日志配置 +# 获取项目根目录（假设本文件在src/chat/message_receive/下，根目录为上上上级目录） +PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../..")) + +ENABLE_S4U_CHAT = os.path.isfile(os.path.join(PROJECT_ROOT, "s4u.s4u")) + +if ENABLE_S4U_CHAT: + print("""\nS4U私聊模式已开启\n!!!!!!!!!!!!!!!!!\n""") + # 仅内部开启 # 配置主程序日志格式 logger = get_logger("chat") +def _check_ban_words(text: str, chat: ChatStream, userinfo: UserInfo) -> bool: + """检查消息是否包含过滤词 + + Args: + text: 待检查的文本 + chat: 聊天对象 + userinfo: 用户信息 + + Returns: + bool: 是否包含过滤词 + """ + for word in global_config.message_receive.ban_words: + if word in text: + chat_name = chat.group_info.group_name if chat.group_info else "私聊" + logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}") + logger.info(f"[过滤词识别]消息中含有{word}，filtered") + return True + return False + + +def _check_ban_regex(text: str, chat: ChatStream, userinfo: UserInfo) -> bool: + """检查消息是否匹配过滤正则表达式 + + Args: + text: 待检查的文本 + chat: 聊天对象 + userinfo: 用户信息 + + Returns: + bool: 是否匹配过滤正则 + """ + for pattern in global_config.message_receive.ban_msgs_regex: + if re.search(pattern, text): + chat_name = chat.group_info.group_name if chat.group_info else "私聊" + logger.info(f"[{chat_name}]{userinfo.user_nickname}:{text}") + logger.info(f"[正则表达式过滤]消息匹配到{pattern}，filtered") + return True + return False + + class ChatBot: def __init__(self): self.bot = None # bot 实例引用 @@ -30,6 +83,7 @@ class ChatBot: # 创建初始化PFC管理器的任务，会在_ensure_started时执行 self.only_process_chat = MessageProcessor() self.pfc_manager = PFCManager.get_instance() + self.s4u_message_processor = S4UMessageProcessor() async def _ensure_started(self): """确保所有任务已启动""" @@ -38,17 +92,6 @@ class ChatBot: self._started = True - async def _create_pfc_chat(self, message: MessageRecv): - try: - if global_config.experimental.pfc_chatting: - chat_id = str(message.chat_stream.stream_id) - private_name = str(message.message_info.user_info.user_nickname) - - await self.pfc_manager.get_or_create_conversation(chat_id, private_name) - - except Exception as e: - logger.error(f"创建PFC聊天失败: {e}") - async def _process_commands_with_new_system(self, message: MessageRecv): # sourcery skip: use-named-expression """使用新插件系统处理命令""" @@ -131,16 +174,28 @@ class ChatBot: message = MessageRecv(message_data) group_info = message.message_info.group_info user_info = message.message_info.user_info + if message.message_info.additional_config: + sent_message = message.message_info.additional_config.get("echo", False) + if sent_message: # 这一段只是为了在一切处理前劫持上报的自身消息，用于更新message_id，需要ada支持上报事件，实际测试中不会对正常使用造成任何问题 + await MessageStorage.update_message(message) + return + get_chat_manager().register_message(message) - # 创建聊天流 chat = await get_chat_manager().get_or_create_stream( platform=message.message_info.platform, user_info=user_info, group_info=group_info, ) + message.update_chat_stream(chat) + # 过滤检查 + if _check_ban_words(message.processed_plain_text, chat, user_info) or _check_ban_regex( + message.raw_message, chat, user_info + ): + return + # 处理消息内容，生成纯文本 await message.process() @@ -166,24 +221,12 @@ class ChatBot: template_group_name = None async def preprocess(): - logger.debug("开始预处理消息...") - # 如果在私聊中 - if group_info is None: - logger.debug("检测到私聊消息") - if global_config.experimental.pfc_chatting: - logger.debug("进入PFC私聊处理流程") - # 创建聊天流 - logger.debug(f"为{user_info.user_id}创建/获取聊天流") - await self.only_process_chat.process_message(message) - await self._create_pfc_chat(message) - # 禁止PFC，进入普通的心流消息处理逻辑 - else: - logger.debug("进入普通心流私聊处理") - await self.heartflow_message_receiver.process_message(message) - # 群聊默认进入心流消息处理逻辑 - else: - logger.debug(f"检测到群聊消息，群ID: {group_info.group_id}") - await self.heartflow_message_receiver.process_message(message) + if ENABLE_S4U_CHAT: + logger.info("进入S4U流程") + await self.s4u_message_processor.process_message(message) + return + + await self.heartflow_message_receiver.process_message(message) if template_group_name: async with global_prompt_manager.async_message_scope(template_group_name): diff --git a/src/chat/message_receive/chat_stream.py b/src/chat/message_receive/chat_stream.py index 55d296db9..a82acc413 100644 --- a/src/chat/message_receive/chat_stream.py +++ b/src/chat/message_receive/chat_stream.py @@ -47,6 +47,16 @@ class ChatMessageContext: return False return True + def get_priority_mode(self) -> str: + """获取优先级模式""" + return self.message.priority_mode + + def get_priority_info(self) -> Optional[dict]: + """获取优先级信息""" + if hasattr(self.message, "priority_info") and self.message.priority_info: + return self.message.priority_info + return None + class ChatStream: """聊天流对象，存储一个完整的聊天上下文""" diff --git a/src/chat/message_receive/message.py b/src/chat/message_receive/message.py index 5798eb512..ef68d7852 100644 --- a/src/chat/message_receive/message.py +++ b/src/chat/message_receive/message.py @@ -108,6 +108,9 @@ class MessageRecv(Message): self.detailed_plain_text = message_dict.get("detailed_plain_text", "") self.is_emoji = False self.is_picid = False + self.is_mentioned = 0.0 + self.priority_mode = "interest" + self.priority_info = None def update_chat_stream(self, chat_stream: "ChatStream"): self.chat_stream = chat_stream @@ -146,8 +149,27 @@ class MessageRecv(Message): if isinstance(segment.data, str): return await get_image_manager().get_emoji_description(segment.data) return "[发了一个表情包，网卡了加载不出来]" + elif segment.type == "mention_bot": + self.is_mentioned = float(segment.data) + return "" + elif segment.type == "set_priority_mode": + # 处理设置优先级模式的消息段 + if isinstance(segment.data, str): + self.priority_mode = segment.data + return "" + elif segment.type == "priority_info": + if isinstance(segment.data, dict): + # 处理优先级信息 + self.priority_info = segment.data + """ + { + 'message_type': 'vip', # vip or normal + 'message_priority': 1.0, # 优先级，大为优先，float + } + """ + return "" else: - return f"[{segment.type}:{str(segment.data)}]" + return "" except Exception as e: logger.error(f"处理消息段失败: {str(e)}, 类型: {segment.type}, 数据: {segment.data}") return f"[处理失败的{segment.type}消息]" @@ -283,6 +305,7 @@ class MessageSending(MessageProcessBase): is_emoji: bool = False, thinking_start_time: float = 0, apply_set_reply_logic: bool = False, + reply_to: str = None, ): # 调用父类初始化 super().__init__( @@ -301,6 +324,8 @@ class MessageSending(MessageProcessBase): self.is_emoji = is_emoji self.apply_set_reply_logic = apply_set_reply_logic + self.reply_to = reply_to + # 用于显示发送内容与显示不一致的情况 self.display_message = display_message diff --git a/src/chat/message_receive/message_sender.py b/src/chat/message_receive/message_sender.py index 6cb256d32..aa6721db3 100644 --- a/src/chat/message_receive/message_sender.py +++ b/src/chat/message_receive/message_sender.py @@ -9,7 +9,6 @@ from src.common.message.api import get_global_api from .message import MessageSending, MessageThinking, MessageSet from src.chat.message_receive.storage import MessageStorage -from ...config.config import global_config from ..utils.utils import truncate_message, calculate_typing_time, count_messages_between from src.common.logger import get_logger @@ -192,20 +191,6 @@ class MessageManager: container = await self.get_container(chat_stream.stream_id) container.add_message(message) - def check_if_sending_message_exist(self, chat_id, thinking_id): - """检查指定聊天流的容器中是否存在具有特定 thinking_id 的 MessageSending 消息 或 emoji 消息""" - # 这个方法现在是非异步的，因为它只读取数据 - container = self.containers.get(chat_id) # 直接 get，因为读取不需要锁 - if container and container.has_messages(): - for message in container.get_all_messages(): - if isinstance(message, MessageSending): - msg_id = getattr(message.message_info, "message_id", None) - # 检查 message_id 是否匹配 thinking_id 或以 "me" 开头 (emoji) - if msg_id == thinking_id or (msg_id and msg_id.startswith("me")): - # logger.debug(f"检查到存在相同thinking_id或emoji的消息: {msg_id} for {thinking_id}") - return True - return False - async def _handle_sending_message(self, container: MessageContainer, message: MessageSending): """处理单个 MessageSending 消息 (包含 set_reply 逻辑)""" try: @@ -216,12 +201,7 @@ class MessageManager: thinking_messages_count, thinking_messages_length = count_messages_between( start_time=thinking_start_time, end_time=now_time, stream_id=message.chat_stream.stream_id ) - # print(f"message.reply:{message.reply}") - # --- 条件应用 set_reply 逻辑 --- - # logger.debug( - # f"[message.apply_set_reply_logic:{message.apply_set_reply_logic},message.is_head:{message.is_head},thinking_messages_count:{thinking_messages_count},thinking_messages_length:{thinking_messages_length},message.is_private_message():{message.is_private_message()}]" - # ) if ( message.is_head and (thinking_messages_count > 3 or thinking_messages_length > 200) @@ -277,14 +257,6 @@ class MessageManager: flush=True, ) - # 检查是否超时 - if thinking_time > global_config.normal_chat.thinking_timeout: - logger.warning( - f"[{chat_id}] 消息思考超时 ({thinking_time:.1f}秒)，移除消息 {message_earliest.message_info.message_id}" - ) - container.remove_message(message_earliest) - print() # 超时后换行，避免覆盖下一条日志 - elif isinstance(message_earliest, MessageSending): # --- 处理发送消息 --- await self._handle_sending_message(container, message_earliest) @@ -301,12 +273,6 @@ class MessageManager: logger.info(f"[{chat_id}] 处理超时发送消息: {msg.message_info.message_id}") await self._handle_sending_message(container, msg) # 复用处理逻辑 - # 清理空容器 (可选) - # async with self._container_lock: - # if not container.has_messages() and chat_id in self.containers: - # logger.debug(f"[{chat_id}] 容器已空，准备移除。") - # del self.containers[chat_id] - async def _start_processor_loop(self): """消息处理器主循环""" while self._running: diff --git a/src/chat/message_receive/storage.py b/src/chat/message_receive/storage.py index ac7818842..862354db7 100644 --- a/src/chat/message_receive/storage.py +++ b/src/chat/message_receive/storage.py @@ -35,9 +35,13 @@ class MessageStorage: filtered_display_message = re.sub(pattern, "", display_message, flags=re.DOTALL) else: filtered_display_message = "" + + reply_to = message.reply_to else: filtered_display_message = "" + reply_to = "" + chat_info_dict = chat_stream.to_dict() user_info_dict = message.message_info.user_info.to_dict() @@ -54,6 +58,7 @@ class MessageStorage: time=float(message.message_info.time), chat_id=chat_stream.stream_id, # Flattened chat_info + reply_to=reply_to, chat_info_stream_id=chat_info_dict.get("stream_id"), chat_info_platform=chat_info_dict.get("platform"), chat_info_user_platform=user_info_from_chat.get("platform"), @@ -101,5 +106,33 @@ class MessageStorage: except Exception: logger.exception("删除撤回消息失败") + # 如果需要其他存储相关的函数，可以在这里添加 + @staticmethod + async def update_message( + message: MessageRecv, + ) -> None: # 用于实时更新数据库的自身发送消息ID，目前能处理text,reply,image和emoji + """更新最新一条匹配消息的message_id""" + try: + if message.message_segment.type == "notify": + mmc_message_id = message.message_segment.data.get("echo") + qq_message_id = message.message_segment.data.get("actual_id") + else: + logger.info(f"更新消息ID错误，seg类型为{message.message_segment.type}") + return + if not qq_message_id: + logger.info("消息不存在message_id，无法更新") + return + # 查询最新一条匹配消息 + matched_message = ( + Messages.select().where((Messages.message_id == mmc_message_id)).order_by(Messages.time.desc()).first() + ) -# 如果需要其他存储相关的函数，可以在这里添加 + if matched_message: + # 更新找到的消息记录 + Messages.update(message_id=qq_message_id).where(Messages.id == matched_message.id).execute() + logger.info(f"更新消息ID成功: {matched_message.message_id} -> {qq_message_id}") + else: + logger.debug("未找到匹配的消息") + + except Exception as e: + logger.error(f"更新消息ID失败: {e}") diff --git a/src/chat/normal_chat/normal_chat.py b/src/chat/normal_chat/normal_chat.py index 2b9777fba..a737d5bec 100644 --- a/src/chat/normal_chat/normal_chat.py +++ b/src/chat/normal_chat/normal_chat.py @@ -1,28 +1,21 @@ import asyncio import time -import traceback from random import random -from typing import List, Optional, Dict # 导入类型提示 +from typing import List, Dict, Optional import os import pickle from maim_message import UserInfo, Seg from src.common.logger import get_logger -from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info -from src.manager.mood_manager import mood_manager from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager from src.chat.utils.timer_calculator import Timer + from src.chat.utils.prompt_builder import global_prompt_manager -from .normal_chat_generator import NormalChatGenerator from ..message_receive.message import MessageSending, MessageRecv, MessageThinking, MessageSet from src.chat.message_receive.message_sender import message_manager from src.chat.normal_chat.willing.willing_manager import get_willing_manager from src.chat.normal_chat.normal_chat_utils import get_recent_message_stats from src.config.config import global_config from src.chat.focus_chat.planners.action_manager import ActionManager -from src.chat.normal_chat.normal_chat_planner import NormalChatPlanner -from src.chat.normal_chat.normal_chat_action_modifier import NormalChatActionModifier -from src.chat.normal_chat.normal_chat_expressor import NormalChatExpressor -from src.chat.replyer.default_generator import DefaultReplyer from src.person_info.person_info import PersonInfoManager from src.person_info.relationship_manager import get_relationship_manager from src.chat.utils.chat_message_builder import ( @@ -31,6 +24,15 @@ from src.chat.utils.chat_message_builder import ( get_raw_msg_before_timestamp_with_chat, num_new_messages_since, ) +from .priority_manager import PriorityManager +import traceback + +from .normal_chat_generator import NormalChatGenerator +from src.chat.normal_chat.normal_chat_planner import NormalChatPlanner +from src.chat.normal_chat.normal_chat_action_modifier import NormalChatActionModifier + +from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info +from src.manager.mood_manager import mood_manager willing_manager = get_willing_manager() @@ -46,16 +48,28 @@ SEGMENT_CLEANUP_CONFIG = { class NormalChat: - def __init__(self, chat_stream: ChatStream, interest_dict: dict = None, on_switch_to_focus_callback=None): - """初始化 NormalChat 实例。只进行同步操作。""" + """ + 普通聊天处理类，负责处理非核心对话的聊天逻辑。 + 每个聊天（私聊或群聊）都会有一个独立的NormalChat实例。 + """ + def __init__( + self, + chat_stream: ChatStream, + interest_dict: dict = None, + on_switch_to_focus_callback=None, + get_cooldown_progress_callback=None, + ): + """ + 初始化NormalChat实例。 + + Args: + chat_stream (ChatStream): 聊天流对象，包含与特定聊天相关的所有信息。 + """ self.chat_stream = chat_stream self.stream_id = chat_stream.stream_id - self.stream_name = get_chat_manager().get_stream_name(self.stream_id) or self.stream_id - # 初始化Normal Chat专用表达器 - self.expressor = NormalChatExpressor(self.chat_stream) - self.replyer = DefaultReplyer(self.chat_stream) + self.stream_name = get_chat_manager().get_stream_name(self.stream_id) or self.stream_id # Interest dict self.interest_dict = interest_dict @@ -69,7 +83,7 @@ class NormalChat: self.gpt = NormalChatGenerator() self.mood_manager = mood_manager self.start_time = time.time() - self._chat_task: Optional[asyncio.Task] = None + self._initialized = False # Track initialization status # Planner相关初始化 @@ -98,13 +112,45 @@ class NormalChat: # 添加回调函数，用于在满足条件时通知切换到focus_chat模式 self.on_switch_to_focus_callback = on_switch_to_focus_callback + # 添加回调函数，用于获取冷却进度 + self.get_cooldown_progress_callback = get_cooldown_progress_callback + self._disabled = False # 增加停用标志 + self.timeout_count = 0 + # 加载持久化的缓存 self._load_cache() logger.debug(f"[{self.stream_name}] NormalChat 初始化完成 (异步部分)。") + self.action_type: Optional[str] = None # 当前动作类型 + self.is_parallel_action: bool = False # 是否是可并行动作 + + # 任务管理 + self._chat_task: Optional[asyncio.Task] = None + self._disabled = False # 停用标志 + + # 新增：回复模式和优先级管理器 + self.reply_mode = self.chat_stream.context.get_priority_mode() + if self.reply_mode == "priority": + interest_dict = interest_dict or {} + self.priority_manager = PriorityManager( + interest_dict=interest_dict, + normal_queue_max_size=5, + ) + else: + self.priority_manager = None + + async def disable(self): + """停用 NormalChat 实例，停止所有后台任务""" + self._disabled = True + if self._chat_task and not self._chat_task.done(): + self._chat_task.cancel() + if self.reply_mode == "priority" and self._priority_chat_task and not self._priority_chat_task.done(): + self._priority_chat_task.cancel() + logger.info(f"[{self.stream_name}] NormalChat 已停用。") + # ================================ # 缓存管理模块 # 负责持久化存储、状态管理、缓存读写 @@ -405,6 +451,60 @@ class NormalChat: f"[{self.stream_name}] 更新用户 {person_id} 的消息段，消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(msg_time))}" ) + async def _priority_chat_loop_add_message(self): + while not self._disabled: + try: + ids = list(self.interest_dict.keys()) + for msg_id in ids: + message, interest_value, _ = self.interest_dict[msg_id] + if not self._disabled: + # 更新消息段信息 + self._update_user_message_segments(message) + + # 添加消息到优先级管理器 + if self.priority_manager: + self.priority_manager.add_message(message, interest_value) + self.interest_dict.pop(msg_id, None) + except Exception: + logger.error( + f"[{self.stream_name}] 优先级聊天循环添加消息时出现错误: {traceback.format_exc()}", exc_info=True + ) + print(traceback.format_exc()) + # 出现错误时，等待一段时间再重试 + raise + await asyncio.sleep(0.1) + + async def _priority_chat_loop(self): + """ + 使用优先级队列的消息处理循环。 + """ + while not self._disabled: + try: + if not self.priority_manager.is_empty(): + # 获取最高优先级的消息 + message = self.priority_manager.get_highest_priority_message() + + if message: + logger.info( + f"[{self.stream_name}] 从队列中取出消息进行处理: User {message.message_info.user_info.user_id}, Time: {time.strftime('%H:%M:%S', time.localtime(message.message_info.time))}" + ) + + # 检查是否有用户满足关系构建条件 + asyncio.create_task(self._check_relation_building_conditions(message)) + + await self.reply_one_message(message) + + # 等待一段时间再检查队列 + await asyncio.sleep(1) + + except asyncio.CancelledError: + logger.info(f"[{self.stream_name}] 优先级聊天循环被取消。") + break + except Exception: + logger.error(f"[{self.stream_name}] 优先级聊天循环出现错误: {traceback.format_exc()}", exc_info=True) + # 出现错误时，等待更长时间避免频繁报错 + await asyncio.sleep(10) + # 改为实例方法 async def _create_thinking_message(self, message: MessageRecv, timestamp: Optional[float] = None) -> str: """创建思考消息""" @@ -602,30 +702,46 @@ class NormalChat: # 改为实例方法, 移除 chat 参数 async def normal_response(self, message: MessageRecv, is_mentioned: bool, interested_rate: float) -> None: - # 新增：如果已停用，直接返回 + """ + 处理接收到的消息。 + 根据回复模式，决定是立即处理还是放入优先级队列。 + """ if self._disabled: - logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。") return - # 新增：在auto模式下检查是否需要直接切换到focus模式 + # 根据回复模式决定行为 + if self.reply_mode == "priority": + # 优先模式下，所有消息都进入管理器 + if self.priority_manager: + self.priority_manager.add_message(message) + return + + # 新增：在auto模式下检查是否需要直接切换到focus模式 if global_config.chat.chat_mode == "auto": - should_switch = await self._check_should_switch_to_focus() - if should_switch: - logger.info(f"[{self.stream_name}] 检测到切换到focus聊天模式的条件，直接执行切换") + if await self._check_should_switch_to_focus(): + logger.info(f"[{self.stream_name}] 检测到切换到focus聊天模式的条件，尝试执行切换") if self.on_switch_to_focus_callback: - await self.on_switch_to_focus_callback() - return + switched_successfully = await self.on_switch_to_focus_callback() + if switched_successfully: + logger.info(f"[{self.stream_name}] 成功切换到focus模式，中止NormalChat处理") + return + else: + logger.info(f"[{self.stream_name}] 切换到focus模式失败（可能在冷却中），继续NormalChat处理") else: logger.warning(f"[{self.stream_name}] 没有设置切换到focus聊天模式的回调函数，无法执行切换") - # 执行定期清理 - self._cleanup_old_segments() + # --- 以下为原有的 "兴趣" 模式逻辑 --- + await self._process_message(message, is_mentioned, interested_rate) - # 更新消息段信息 - self._update_user_message_segments(message) + async def _process_message(self, message: MessageRecv, is_mentioned: bool, interested_rate: float) -> None: + """ + 实际处理单条消息的逻辑，包括意愿判断、回复生成、动作执行等。 + """ + if self._disabled: + return # 检查是否有用户满足关系构建条件 - asyncio.create_task(self._check_relation_building_conditions()) + asyncio.create_task(self._check_relation_building_conditions(message)) timing_results = {} reply_probability = ( @@ -647,6 +763,21 @@ class NormalChat: reply_probability += message.message_info.additional_config["maimcore_reply_probability_gain"] reply_probability = min(max(reply_probability, 0), 1) # 确保概率在 0-1 之间 + # 处理表情包 + if message.is_emoji or message.is_picid: + reply_probability = 0 + + # 应用疲劳期回复频率调整 + fatigue_multiplier = self._get_fatigue_reply_multiplier() + original_probability = reply_probability + reply_probability *= fatigue_multiplier + + # 如果应用了疲劳调整，记录日志 + if fatigue_multiplier < 1.0: + logger.info( + f"[{self.stream_name}] 疲劳期回复频率调整: {original_probability * 100:.1f}% -> {reply_probability * 100:.1f}% (系数: {fatigue_multiplier:.2f})" + ) + # 打印消息信息 mes_name = self.chat_stream.group_info.group_name if self.chat_stream.group_info else "私聊" # current_time = time.strftime("%H:%M:%S", time.localtime(message.message_info.time)) @@ -660,175 +791,10 @@ class NormalChat: do_reply = False response_set = None # 初始化 response_set if random() < reply_probability: - do_reply = True - - # 回复前处理 - await willing_manager.before_generate_reply_handle(message.message_info.message_id) - - thinking_id = await self._create_thinking_message(message) - - # 如果启用planner，预先修改可用actions（避免在并行任务中重复调用） - available_actions = None - if self.enable_planner: - try: - await self.action_modifier.modify_actions_for_normal_chat( - self.chat_stream, self.recent_replies, message.processed_plain_text - ) - available_actions = self.action_manager.get_using_actions_for_mode("normal") - except Exception as e: - logger.warning(f"[{self.stream_name}] 获取available_actions失败: {e}") - available_actions = None - - # 定义并行执行的任务 - async def generate_normal_response(): - """生成普通回复""" - try: - return await self.gpt.generate_response( - message=message, - thinking_id=thinking_id, - enable_planner=self.enable_planner, - available_actions=available_actions, - ) - except Exception as e: - logger.error(f"[{self.stream_name}] 回复生成出现错误：{str(e)} {traceback.format_exc()}") - return None - - async def plan_and_execute_actions(): - """规划和执行额外动作""" - if not self.enable_planner: - logger.debug(f"[{self.stream_name}] Planner未启用，跳过动作规划") - return None - - try: - # 获取发送者名称（动作修改已在并行执行前完成） - sender_name = self._get_sender_name(message) - - no_action = { - "action_result": { - "action_type": "no_action", - "action_data": {}, - "reasoning": "规划器初始化默认", - "is_parallel": True, - }, - "chat_context": "", - "action_prompt": "", - } - - # 检查是否应该跳过规划 - if self.action_modifier.should_skip_planning(): - logger.debug(f"[{self.stream_name}] 没有可用动作，跳过规划") - self.action_type = "no_action" - return no_action - - # 执行规划 - plan_result = await self.planner.plan(message, sender_name) - action_type = plan_result["action_result"]["action_type"] - action_data = plan_result["action_result"]["action_data"] - reasoning = plan_result["action_result"]["reasoning"] - is_parallel = plan_result["action_result"].get("is_parallel", False) - - logger.info( - f"[{self.stream_name}] Planner决策: {action_type}, 理由: {reasoning}, 并行执行: {is_parallel}" - ) - self.action_type = action_type # 更新实例属性 - self.is_parallel_action = is_parallel # 新增：保存并行执行标志 - - # 如果规划器决定不执行任何动作 - if action_type == "no_action": - logger.debug(f"[{self.stream_name}] Planner决定不执行任何额外动作") - return no_action - - # 执行额外的动作（不影响回复生成） - action_result = await self._execute_action(action_type, action_data, message, thinking_id) - if action_result is not None: - logger.info(f"[{self.stream_name}] 额外动作 {action_type} 执行完成") - else: - logger.warning(f"[{self.stream_name}] 额外动作 {action_type} 执行失败") - - return { - "action_type": action_type, - "action_data": action_data, - "reasoning": reasoning, - "is_parallel": is_parallel, - } - - except Exception as e: - logger.error(f"[{self.stream_name}] Planner执行失败: {e}") - return no_action - - # 并行执行回复生成和动作规划 - self.action_type = None # 初始化动作类型 - self.is_parallel_action = False # 初始化并行动作标志 - with Timer("并行生成回复和规划", timing_results): - response_set, plan_result = await asyncio.gather( - generate_normal_response(), plan_and_execute_actions(), return_exceptions=True - ) - - # 处理生成回复的结果 - if isinstance(response_set, Exception): - logger.error(f"[{self.stream_name}] 回复生成异常: {response_set}") - response_set = None - - # 处理规划结果（可选，不影响回复） - if isinstance(plan_result, Exception): - logger.error(f"[{self.stream_name}] 动作规划异常: {plan_result}") - elif plan_result: - logger.debug(f"[{self.stream_name}] 额外动作处理完成: {self.action_type}") - - if not response_set or ( - self.enable_planner and self.action_type not in ["no_action"] and not self.is_parallel_action - ): - if not response_set: - logger.info(f"[{self.stream_name}] 模型未生成回复内容") - elif self.enable_planner and self.action_type not in ["no_action"] and not self.is_parallel_action: - logger.info(f"[{self.stream_name}] 模型选择其他动作（非并行动作）") - # 如果模型未生成回复，移除思考消息 - container = await message_manager.get_container(self.stream_id) # 使用 self.stream_id - for msg in container.messages[:]: - if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: - container.messages.remove(msg) - logger.debug(f"[{self.stream_name}] 已移除未产生回复的思考消息 {thinking_id}") - break - # 需要在此处也调用 not_reply_handle 和 delete 吗？ - # 如果是因为模型没回复，也算是一种 "未回复" - await willing_manager.not_reply_handle(message.message_info.message_id) - willing_manager.delete(message.message_info.message_id) - return # 不执行后续步骤 - - # logger.info(f"[{self.stream_name}] 回复内容: {response_set}") - - if self._disabled: - logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。") - return - - # 发送回复 (不再需要传入 chat) - with Timer("消息发送", timing_results): - first_bot_msg = await self._add_messages_to_manager(message, response_set, thinking_id) - - # 检查 first_bot_msg 是否为 None (例如思考消息已被移除的情况) - if first_bot_msg: - # 消息段已在接收消息时更新，这里不需要额外处理 - - # 记录回复信息到最近回复列表中 - reply_info = { - "time": time.time(), - "user_message": message.processed_plain_text, - "user_info": { - "user_id": message.message_info.user_info.user_id, - "user_nickname": message.message_info.user_info.user_nickname, - }, - "response": response_set, - "is_mentioned": is_mentioned, - "is_reference_reply": message.reply is not None, # 判断是否为引用回复 - "timing": {k: round(v, 2) for k, v in timing_results.items()}, - } - self.recent_replies.append(reply_info) - # 保持最近回复历史在限定数量内 - if len(self.recent_replies) > self.max_replies_history: - self.recent_replies = self.recent_replies[-self.max_replies_history :] - - # 回复后处理 - await willing_manager.after_generate_reply_handle(message.message_info.message_id) + with Timer("获取回复", timing_results): + await willing_manager.before_generate_reply_handle(message.message_info.message_id) + do_reply = await self.reply_one_message(message) + response_set = do_reply if do_reply else None # 输出性能计时结果 if do_reply and response_set: # 确保 response_set 不是 None @@ -838,6 +804,7 @@ class NormalChat: logger.info( f"[{self.stream_name}]回复消息: {trigger_msg[:30]}... | 回复内容: {response_msg[:30]}... | 计时: {timing_str}" ) + await willing_manager.after_generate_reply_handle(message.message_info.message_id) elif not do_reply: # 不回复处理 await willing_manager.not_reply_handle(message.message_info.message_id) @@ -845,6 +812,183 @@ class NormalChat: # 意愿管理器：注销当前message信息 (无论是否回复，只要处理过就删除) willing_manager.delete(message.message_info.message_id) + async def reply_one_message(self, message: MessageRecv) -> None: + # 回复前处理 + thinking_id = await self._create_thinking_message(message) + + # 如果启用planner，预先修改可用actions（避免在并行任务中重复调用） + available_actions = None + if self.enable_planner: + try: + await self.action_modifier.modify_actions_for_normal_chat( + self.chat_stream, self.recent_replies, message.processed_plain_text + ) + available_actions = self.action_manager.get_using_actions_for_mode("normal") + except Exception as e: + logger.warning(f"[{self.stream_name}] 获取available_actions失败: {e}") + available_actions = None + + # 定义并行执行的任务 + async def generate_normal_response(): + """生成普通回复""" + try: + return await self.gpt.generate_response( + message=message, + available_actions=available_actions, + ) + except Exception as e: + logger.error(f"[{self.stream_name}] 回复生成出现错误：{str(e)} {traceback.format_exc()}") + return None + + async def plan_and_execute_actions(): + """规划和执行额外动作""" + if not self.enable_planner: + logger.debug(f"[{self.stream_name}] Planner未启用，跳过动作规划") + return None + + try: + no_action = { + "action_result": { + "action_type": "no_action", + "action_data": {}, + "reasoning": "规划器初始化默认", + "is_parallel": True, + }, + "chat_context": "", + "action_prompt": "", + } + + # 检查是否应该跳过规划 + if self.action_modifier.should_skip_planning(): + logger.debug(f"[{self.stream_name}] 没有可用动作，跳过规划") + self.action_type = "no_action" + return no_action + + # 执行规划 + plan_result = await self.planner.plan(message) + action_type = plan_result["action_result"]["action_type"] + action_data = plan_result["action_result"]["action_data"] + reasoning = plan_result["action_result"]["reasoning"] + is_parallel = plan_result["action_result"].get("is_parallel", False) + + logger.info( + f"[{self.stream_name}] Planner决策: {action_type}, 理由: {reasoning}, 并行执行: {is_parallel}" + ) + self.action_type = action_type # 更新实例属性 + self.is_parallel_action = is_parallel # 新增：保存并行执行标志 + + # 如果规划器决定不执行任何动作 + if action_type == "no_action": + logger.debug(f"[{self.stream_name}] Planner决定不执行任何额外动作") + return no_action + + # 执行额外的动作（不影响回复生成） + action_result = await self._execute_action(action_type, action_data, message, thinking_id) + if action_result is not None: + logger.info(f"[{self.stream_name}] 额外动作 {action_type} 执行完成") + else: + logger.warning(f"[{self.stream_name}] 额外动作 {action_type} 执行失败") + + return { + "action_type": action_type, + "action_data": action_data, + "reasoning": reasoning, + "is_parallel": is_parallel, + } + + except Exception as e: + logger.error(f"[{self.stream_name}] Planner执行失败: {e}") + return no_action + + # 并行执行回复生成和动作规划 + self.action_type = None # 初始化动作类型 + self.is_parallel_action = False # 初始化并行动作标志 + + gen_task = asyncio.create_task(generate_normal_response()) + plan_task = asyncio.create_task(plan_and_execute_actions()) + + try: + gather_timeout = global_config.normal_chat.thinking_timeout + results = await asyncio.wait_for( + asyncio.gather(gen_task, plan_task, return_exceptions=True), + timeout=gather_timeout, + ) + response_set, plan_result = results + except asyncio.TimeoutError: + logger.warning( + f"[{self.stream_name}] 并行执行回复生成和动作规划超时 ({gather_timeout}秒)，正在取消相关任务..." + ) + self.timeout_count += 1 + if self.timeout_count > 5: + logger.error( + f"[{self.stream_name}] 连续回复超时，{global_config.normal_chat.thinking_timeout}秒 内大模型没有返回有效内容，请检查你的api是否速度过慢或配置错误。建议不要使用推理模型，推理模型生成速度过慢。" + ) + return False + + # 取消未完成的任务 + if not gen_task.done(): + gen_task.cancel() + if not plan_task.done(): + plan_task.cancel() + + # 清理思考消息 + await self._cleanup_thinking_message_by_id(thinking_id) + + response_set = None + plan_result = None + + # 处理生成回复的结果 + if isinstance(response_set, Exception): + logger.error(f"[{self.stream_name}] 回复生成异常: {response_set}") + response_set = None + + # 处理规划结果（可选，不影响回复） + if isinstance(plan_result, Exception): + logger.error(f"[{self.stream_name}] 动作规划异常: {plan_result}") + elif plan_result: + logger.debug(f"[{self.stream_name}] 额外动作处理完成: {self.action_type}") + + if not response_set or ( + self.enable_planner and self.action_type not in ["no_action"] and not self.is_parallel_action + ): + if not response_set: + logger.info(f"[{self.stream_name}] 模型未生成回复内容") + elif self.enable_planner and self.action_type not in ["no_action"] and not self.is_parallel_action: + logger.info(f"[{self.stream_name}] 模型选择其他动作（非并行动作）") + # 如果模型未生成回复，移除思考消息 + await self._cleanup_thinking_message_by_id(thinking_id) + return False + + # logger.info(f"[{self.stream_name}] 回复内容: {response_set}") + + if self._disabled: + logger.info(f"[{self.stream_name}] 已停用，忽略 normal_response。") + return False + + # 发送回复 (不再需要传入 chat) + first_bot_msg = await self._add_messages_to_manager(message, response_set, thinking_id) + + # 检查 first_bot_msg 是否为 None (例如思考消息已被移除的情况) + if first_bot_msg: + # 消息段已在接收消息时更新，这里不需要额外处理 + + # 记录回复信息到最近回复列表中 + reply_info = { + "time": time.time(), + "user_message": message.processed_plain_text, + "user_info": { + "user_id": message.message_info.user_info.user_id, + "user_nickname": message.message_info.user_info.user_nickname, + }, + "response": response_set, + "is_reference_reply": message.reply is not None, # 判断是否为引用回复 + } + self.recent_replies.append(reply_info) + # 保持最近回复历史在限定数量内 + if len(self.recent_replies) > self.max_replies_history: + self.recent_replies = self.recent_replies[-self.max_replies_history :] + return response_set if response_set else False + # 改为实例方法, 移除 chat 参数 async def start_chat(self): @@ -864,8 +1008,16 @@ class NormalChat: self._chat_task = None try: - logger.debug(f"[{self.stream_name}] 创建新的聊天轮询任务") - polling_task = asyncio.create_task(self._reply_interested_message()) + logger.info(f"[{self.stream_name}] 创建新的聊天轮询任务，模式: {self.reply_mode}") + if self.reply_mode == "priority": + polling_task_send = asyncio.create_task(self._priority_chat_loop()) + polling_task_recv = asyncio.create_task(self._priority_chat_loop_add_message()) + print("555") + polling_task = asyncio.gather(polling_task_send, polling_task_recv) + print("666") + + else: # 默认或 "interest" 模式 + polling_task = asyncio.create_task(self._reply_interested_message()) # 设置回调 polling_task.add_done_callback(lambda t: self._handle_task_completion(t)) @@ -904,7 +1056,7 @@ class NormalChat: # 尝试获取异常，但不抛出 exc = task.exception() if exc: - logger.error(f"[{self.stream_name}] 任务异常: {type(exc).__name__}: {exc}") + logger.error(f"[{self.stream_name}] 任务异常: {type(exc).__name__}: {exc}", exc_info=exc) else: logger.debug(f"[{self.stream_name}] 任务正常完成") except Exception as e: @@ -1056,18 +1208,6 @@ class NormalChat: f"意愿放大器更新为: {self.willing_amplifier:.2f}" ) - def _get_sender_name(self, message: MessageRecv) -> str: - """获取发送者名称，用于planner""" - if message.chat_stream.user_info: - user_info = message.chat_stream.user_info - if user_info.user_cardname and user_info.user_nickname: - return f"[{user_info.user_nickname}][群昵称：{user_info.user_cardname}]" - elif user_info.user_nickname: - return f"[{user_info.user_nickname}]" - else: - return f"用户({user_info.user_id})" - return "某人" - async def _execute_action( self, action_type: str, action_data: dict, message: MessageRecv, thinking_id: str ) -> Optional[bool]: @@ -1104,17 +1244,18 @@ class NormalChat: return False - def set_planner_enabled(self, enabled: bool): - """设置是否启用planner""" - self.enable_planner = enabled - logger.info(f"[{self.stream_name}] Planner {'启用' if enabled else '禁用'}") - def get_action_manager(self) -> ActionManager: """获取动作管理器实例""" return self.action_manager - async def _check_relation_building_conditions(self): + async def _check_relation_building_conditions(self, message: MessageRecv): """检查person_engaged_cache中是否有满足关系构建条件的用户""" + # 执行定期清理 + self._cleanup_old_segments() + + # 更新消息段信息 + self._update_user_message_segments(message) + users_to_build_relationship = [] for person_id, segments in list(self.person_engaged_cache.items()): @@ -1201,6 +1342,30 @@ class NormalChat: logger.error(f"[{self.stream_name}] 为 {person_id} 更新印象时发生错误: {e}") logger.error(traceback.format_exc()) + def _get_fatigue_reply_multiplier(self) -> float: + """获取疲劳期回复频率调整系数 + + Returns: + float: 回复频率调整系数，范围0.5-1.0 + """ + if not self.get_cooldown_progress_callback: + return 1.0 # 没有冷却进度回调，返回正常系数 + + try: + cooldown_progress = self.get_cooldown_progress_callback() + + if cooldown_progress >= 1.0: + return 1.0 # 冷却完成，正常回复频率 + + # 疲劳期间：从0.5逐渐恢复到1.0 + # progress=0时系数为0.5，progress=1时系数为1.0 + multiplier = 0.2 + (0.8 * cooldown_progress) + + return multiplier + except Exception as e: + logger.warning(f"[{self.stream_name}] 获取疲劳调整系数时出错: {e}") + return 1.0 # 出错时返回正常系数 + async def _check_should_switch_to_focus(self) -> bool: """ 检查是否满足切换到focus模式的条件 @@ -1235,3 +1400,16 @@ class NormalChat: ) return should_switch + + async def _cleanup_thinking_message_by_id(self, thinking_id: str): + """根据ID清理思考消息""" + try: + container = await message_manager.get_container(self.stream_id) + if container: + for msg in container.messages[:]: + if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: + container.messages.remove(msg) + logger.info(f"[{self.stream_name}] 已清理思考消息 {thinking_id}") + break + except Exception as e: + logger.error(f"[{self.stream_name}] 清理思考消息 {thinking_id} 时出错: {e}") diff --git a/src/chat/normal_chat/normal_chat_action_modifier.py b/src/chat/normal_chat/normal_chat_action_modifier.py index a3f830861..8cdde145e 100644 --- a/src/chat/normal_chat/normal_chat_action_modifier.py +++ b/src/chat/normal_chat/normal_chat_action_modifier.py @@ -80,7 +80,7 @@ class NormalChatActionModifier: message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=chat_stream.stream_id, timestamp=time.time(), - limit=global_config.focus_chat.observation_context_size, # 使用相同的配置 + limit=global_config.chat.max_context_size, # 使用相同的配置 ) # 构建可读的聊天上下文 diff --git a/src/chat/normal_chat/normal_chat_expressor.py b/src/chat/normal_chat/normal_chat_expressor.py deleted file mode 100644 index c89ad8534..000000000 --- a/src/chat/normal_chat/normal_chat_expressor.py +++ /dev/null @@ -1,262 +0,0 @@ -""" -Normal Chat Expressor - -为Normal Chat专门设计的表达器，不需要经过LLM风格化处理， -直接发送消息，主要用于插件动作中需要发送消息的场景。 -""" - -import time -from typing import List, Optional, Tuple, Dict, Any -from src.chat.message_receive.message import MessageRecv, MessageSending, MessageThinking, Seg -from src.chat.message_receive.message import UserInfo -from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager -from src.chat.message_receive.message_sender import message_manager -from src.config.config import global_config -from src.common.logger import get_logger - -logger = get_logger("normal_chat_expressor") - - -class NormalChatExpressor: - """Normal Chat专用表达器 - - 特点： - 1. 不经过LLM风格化，直接发送消息 - 2. 支持文本和表情包发送 - 3. 为插件动作提供简化的消息发送接口 - 4. 保持与focus_chat expressor相似的API，但去掉复杂的风格化流程 - """ - - def __init__(self, chat_stream: ChatStream): - """初始化Normal Chat表达器 - - Args: - chat_stream: 聊天流对象 - stream_name: 流名称 - """ - self.chat_stream = chat_stream - self.stream_name = get_chat_manager().get_stream_name(self.chat_stream.stream_id) or self.chat_stream.stream_id - self.log_prefix = f"[{self.stream_name}]Normal表达器" - - logger.debug(f"{self.log_prefix} 初始化完成") - - async def create_thinking_message( - self, anchor_message: Optional[MessageRecv], thinking_id: str - ) -> Optional[MessageThinking]: - """创建思考消息 - - Args: - anchor_message: 锚点消息 - thinking_id: 思考ID - - Returns: - MessageThinking: 创建的思考消息，如果失败返回None - """ - if not anchor_message or not anchor_message.chat_stream: - logger.error(f"{self.log_prefix} 无法创建思考消息，缺少有效的锚点消息或聊天流") - return None - - messageinfo = anchor_message.message_info - thinking_time_point = time.time() - - bot_user_info = UserInfo( - user_id=global_config.bot.qq_account, - user_nickname=global_config.bot.nickname, - platform=messageinfo.platform, - ) - - thinking_message = MessageThinking( - message_id=thinking_id, - chat_stream=self.chat_stream, - bot_user_info=bot_user_info, - reply=anchor_message, - thinking_start_time=thinking_time_point, - ) - - await message_manager.add_message(thinking_message) - logger.debug(f"{self.log_prefix} 创建思考消息: {thinking_id}") - return thinking_message - - async def send_response_messages( - self, - anchor_message: Optional[MessageRecv], - response_set: List[Tuple[str, str]], - thinking_id: str = "", - display_message: str = "", - ) -> Optional[MessageSending]: - """发送回复消息 - - Args: - anchor_message: 锚点消息 - response_set: 回复内容集合，格式为 [(type, content), ...] - thinking_id: 思考ID - display_message: 显示消息 - - Returns: - MessageSending: 发送的第一条消息，如果失败返回None - """ - try: - if not response_set: - logger.warning(f"{self.log_prefix} 回复内容为空") - return None - - # 如果没有thinking_id，生成一个 - if not thinking_id: - thinking_time_point = round(time.time(), 2) - thinking_id = "mt" + str(thinking_time_point) - - # 创建思考消息 - if anchor_message: - await self.create_thinking_message(anchor_message, thinking_id) - - # 创建消息集 - - mark_head = False - is_emoji = False - if len(response_set) == 0: - return None - message_id = f"{thinking_id}_{len(response_set)}" - response_type, content = response_set[0] - if len(response_set) > 1: - message_segment = Seg(type="seglist", data=[Seg(type=t, data=c) for t, c in response_set]) - else: - message_segment = Seg(type=response_type, data=content) - if response_type == "emoji": - is_emoji = True - - bot_msg = await self._build_sending_message( - message_id=message_id, - message_segment=message_segment, - thinking_id=thinking_id, - anchor_message=anchor_message, - thinking_start_time=time.time(), - reply_to=mark_head, - is_emoji=is_emoji, - display_message=display_message, - ) - logger.debug(f"{self.log_prefix} 添加{response_type}类型消息: {content}") - - # 提交消息集 - if bot_msg: - await message_manager.add_message(bot_msg) - logger.info( - f"{self.log_prefix} 成功发送 {response_type}类型消息: {str(content)[:200] + '...' if len(str(content)) > 200 else content}" - ) - container = await message_manager.get_container(self.chat_stream.stream_id) # 使用 self.stream_id - for msg in container.messages[:]: - if isinstance(msg, MessageThinking) and msg.message_info.message_id == thinking_id: - container.messages.remove(msg) - logger.debug(f"[{self.stream_name}] 已移除未产生回复的思考消息 {thinking_id}") - break - return bot_msg - else: - logger.warning(f"{self.log_prefix} 没有有效的消息被创建") - return None - - except Exception as e: - logger.error(f"{self.log_prefix} 发送消息失败: {e}") - import traceback - - traceback.print_exc() - return None - - async def _build_sending_message( - self, - message_id: str, - message_segment: Seg, - thinking_id: str, - anchor_message: Optional[MessageRecv], - thinking_start_time: float, - reply_to: bool = False, - is_emoji: bool = False, - display_message: str = "", - ) -> MessageSending: - """构建发送消息 - - Args: - message_id: 消息ID - message_segment: 消息段 - thinking_id: 思考ID - anchor_message: 锚点消息 - thinking_start_time: 思考开始时间 - reply_to: 是否回复 - is_emoji: 是否为表情包 - - Returns: - MessageSending: 构建的发送消息 - """ - bot_user_info = UserInfo( - user_id=global_config.bot.qq_account, - user_nickname=global_config.bot.nickname, - platform=anchor_message.message_info.platform if anchor_message else "unknown", - ) - - message_sending = MessageSending( - message_id=message_id, - chat_stream=self.chat_stream, - bot_user_info=bot_user_info, - message_segment=message_segment, - sender_info=self.chat_stream.user_info, - reply=anchor_message if reply_to else None, - thinking_start_time=thinking_start_time, - is_emoji=is_emoji, - display_message=display_message, - ) - - return message_sending - - async def deal_reply( - self, - cycle_timers: dict, - action_data: Dict[str, Any], - reasoning: str, - anchor_message: MessageRecv, - thinking_id: str, - ) -> Tuple[bool, Optional[str]]: - """处理回复动作 - 兼容focus_chat expressor API - - Args: - cycle_timers: 周期计时器（normal_chat中不使用） - action_data: 动作数据，包含text、target、emojis等 - reasoning: 推理说明 - anchor_message: 锚点消息 - thinking_id: 思考ID - - Returns: - Tuple[bool, Optional[str]]: (是否成功, 回复文本) - """ - try: - response_set = [] - - # 处理文本内容 - text_content = action_data.get("text", "") - if text_content: - response_set.append(("text", text_content)) - - # 处理表情包 - emoji_content = action_data.get("emojis", "") - if emoji_content: - response_set.append(("emoji", emoji_content)) - - if not response_set: - logger.warning(f"{self.log_prefix} deal_reply: 没有有效的回复内容") - return False, None - - # 发送消息 - result = await self.send_response_messages( - anchor_message=anchor_message, - response_set=response_set, - thinking_id=thinking_id, - ) - - if result: - return True, text_content if text_content else "发送成功" - else: - return False, None - - except Exception as e: - logger.error(f"{self.log_prefix} deal_reply执行失败: {e}") - import traceback - - traceback.print_exc() - return False, None diff --git a/src/chat/normal_chat/normal_chat_generator.py b/src/chat/normal_chat/normal_chat_generator.py index 6a3c8cc52..df7cc6876 100644 --- a/src/chat/normal_chat/normal_chat_generator.py +++ b/src/chat/normal_chat/normal_chat_generator.py @@ -1,13 +1,11 @@ -from typing import List, Optional, Union -import random from src.llm_models.utils_model import LLMRequest from src.config.config import global_config from src.chat.message_receive.message import MessageThinking -from src.chat.normal_chat.normal_prompt import prompt_builder -from src.chat.utils.timer_calculator import Timer from src.common.logger import get_logger from src.person_info.person_info import PersonInfoManager, get_person_info_manager from src.chat.utils.utils import process_llm_response +from src.plugin_system.apis import generator_api +from src.chat.focus_chat.memory_activator import MemoryActivator logger = get_logger("normal_chat_response") @@ -15,142 +13,60 @@ logger = get_logger("normal_chat_response") class NormalChatGenerator: def __init__(self): - # TODO: API-Adapter修改标记 - self.model_reasoning = LLMRequest( - model=global_config.model.replyer_1, - request_type="normal.chat_1", - ) - self.model_normal = LLMRequest( - model=global_config.model.replyer_2, - request_type="normal.chat_2", - ) + model_config_1 = global_config.model.replyer_1.copy() + model_config_2 = global_config.model.replyer_2.copy() + + prob_first = global_config.chat.replyer_random_probability + + model_config_1["weight"] = prob_first + model_config_2["weight"] = 1.0 - prob_first + + self.model_configs = [model_config_1, model_config_2] self.model_sum = LLMRequest(model=global_config.model.memory_summary, temperature=0.7, request_type="relation") - self.current_model_type = "r1" # 默认使用 R1 - self.current_model_name = "unknown model" + self.memory_activator = MemoryActivator() async def generate_response( - self, message: MessageThinking, thinking_id: str, enable_planner: bool = False, available_actions=None - ) -> Optional[Union[str, List[str]]]: - """根据当前模型类型选择对应的生成函数""" - # 从global_config中获取模型概率值并选择模型 - if random.random() < global_config.normal_chat.normal_chat_first_probability: - current_model = self.model_reasoning - self.current_model_name = current_model.model_name - else: - current_model = self.model_normal - self.current_model_name = current_model.model_name - - logger.info( - f"{self.current_model_name}思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}" - ) # noqa: E501 - - model_response = await self._generate_response_with_model( - message, current_model, thinking_id, enable_planner, available_actions - ) - - if model_response: - logger.debug(f"{global_config.bot.nickname}的备选回复是：{model_response}") - model_response = process_llm_response(model_response) - - return model_response - else: - logger.info(f"{self.current_model_name}思考，失败") - return None - - async def _generate_response_with_model( self, message: MessageThinking, - model: LLMRequest, - thinking_id: str, - enable_planner: bool = False, available_actions=None, ): + logger.info( + f"NormalChat思考:{message.processed_plain_text[:30] + '...' if len(message.processed_plain_text) > 30 else message.processed_plain_text}" + ) person_id = PersonInfoManager.get_person_id( message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id ) person_info_manager = get_person_info_manager() person_name = await person_info_manager.get_value(person_id, "person_name") - - if message.chat_stream.user_info.user_cardname and message.chat_stream.user_info.user_nickname: - sender_name = ( - f"[{message.chat_stream.user_info.user_nickname}]" - f"[群昵称：{message.chat_stream.user_info.user_cardname}]（你叫ta{person_name}）" - ) - elif message.chat_stream.user_info.user_nickname: - sender_name = f"[{message.chat_stream.user_info.user_nickname}]（你叫ta{person_name}）" - else: - sender_name = f"用户({message.chat_stream.user_info.user_id})" - - # 构建prompt - with Timer() as t_build_prompt: - prompt = await prompt_builder.build_prompt_normal( - message_txt=message.processed_plain_text, - sender_name=sender_name, - chat_stream=message.chat_stream, - enable_planner=enable_planner, - available_actions=available_actions, - ) - logger.debug(f"构建prompt时间: {t_build_prompt.human_readable}") + relation_info = await person_info_manager.get_value(person_id, "short_impression") + reply_to_str = f"{person_name}:{message.processed_plain_text}" try: - content, (reasoning_content, model_name) = await model.generate_response_async(prompt) + success, reply_set, prompt = await generator_api.generate_reply( + chat_stream=message.chat_stream, + reply_to=reply_to_str, + relation_info=relation_info, + available_actions=available_actions, + enable_tool=global_config.tool.enable_in_normal_chat, + model_configs=self.model_configs, + request_type="normal.replyer", + return_prompt=True, + ) - logger.info(f"prompt:{prompt}\n生成回复：{content}") + if not success or not reply_set: + logger.info(f"对 {message.processed_plain_text} 的回复生成失败") + return None - logger.info(f"对 {message.processed_plain_text} 的回复：{content}") + content = " ".join([item[1] for item in reply_set if item[0] == "text"]) + logger.debug(f"对 {message.processed_plain_text} 的回复：{content}") + + if content: + logger.info(f"{global_config.bot.nickname}的备选回复是：{content}") + content = process_llm_response(content) + + return content except Exception: logger.exception("生成回复时出错") return None - - return content - - async def _get_emotion_tags(self, content: str, processed_plain_text: str): - """提取情感标签，结合立场和情绪""" - try: - # 构建提示词，结合回复内容、被回复的内容以及立场分析 - prompt = f""" - 请严格根据以下对话内容，完成以下任务： - 1. 判断回复者对被回复者观点的直接立场： - - "支持"：明确同意或强化被回复者观点 - - "反对"：明确反驳或否定被回复者观点 - - "中立"：不表达明确立场或无关回应 - 2. 从"开心,愤怒,悲伤,惊讶,平静,害羞,恐惧,厌恶,困惑"中选出最匹配的1个情感标签 - 3. 按照"立场-情绪"的格式直接输出结果，例如："反对-愤怒" - 4. 考虑回复者的人格设定为{global_config.personality.personality_core} - - 对话示例： - 被回复：「A就是笨」 - 回复：「A明明很聪明」 → 反对-愤怒 - - 当前对话： - 被回复：「{processed_plain_text}」 - 回复：「{content}」 - - 输出要求： - - 只需输出"立场-情绪"结果，不要解释 - - 严格基于文字直接表达的对立关系判断 - """ - - # 调用模型生成结果 - result, (reasoning_content, model_name) = await self.model_sum.generate_response_async(prompt) - result = result.strip() - - # 解析模型输出的结果 - if "-" in result: - stance, emotion = result.split("-", 1) - valid_stances = ["支持", "反对", "中立"] - valid_emotions = ["开心", "愤怒", "悲伤", "惊讶", "害羞", "平静", "恐惧", "厌恶", "困惑"] - if stance in valid_stances and emotion in valid_emotions: - return stance, emotion # 返回有效的立场-情绪组合 - else: - logger.debug(f"无效立场-情感组合:{result}") - return "中立", "平静" # 默认返回中立-平静 - else: - logger.debug(f"立场-情感格式错误:{result}") - return "中立", "平静" # 格式错误时返回默认值 - - except Exception as e: - logger.debug(f"获取情感标签时出错: {e}") - return "中立", "平静" # 出错时返回默认值 diff --git a/src/chat/normal_chat/normal_chat_planner.py b/src/chat/normal_chat/normal_chat_planner.py index 810df2dd9..9c4e08433 100644 --- a/src/chat/normal_chat/normal_chat_planner.py +++ b/src/chat/normal_chat/normal_chat_planner.py @@ -72,7 +72,7 @@ class NormalChatPlanner: self.action_manager = action_manager - async def plan(self, message: MessageThinking, sender_name: str = "某人") -> Dict[str, Any]: + async def plan(self, message: MessageThinking) -> Dict[str, Any]: """ Normal Chat 规划器: 使用LLM根据上下文决定做出什么动作。 @@ -122,7 +122,7 @@ class NormalChatPlanner: message_list_before_now = get_raw_msg_before_timestamp_with_chat( chat_id=message.chat_stream.stream_id, timestamp=time.time(), - limit=global_config.focus_chat.observation_context_size, + limit=global_config.chat.max_context_size, ) chat_context = build_readable_messages( diff --git a/src/chat/normal_chat/normal_prompt.py b/src/chat/normal_chat/normal_prompt.py deleted file mode 100644 index 75a237882..000000000 --- a/src/chat/normal_chat/normal_prompt.py +++ /dev/null @@ -1,372 +0,0 @@ -from src.config.config import global_config -from src.common.logger import get_logger -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat -import time -from src.chat.utils.utils import get_recent_group_speaker -from src.manager.mood_manager import mood_manager -from src.chat.memory_system.Hippocampus import hippocampus_manager -from src.chat.knowledge.knowledge_lib import qa_manager -import random -from src.person_info.person_info import get_person_info_manager -from src.chat.express.expression_selector import expression_selector -import re -import ast - -from src.person_info.relationship_manager import get_relationship_manager - -logger = get_logger("prompt") - - -def init_prompt(): - Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1") - Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1") - Prompt("在群里聊天", "chat_target_group2") - Prompt("和{sender_name}私聊", "chat_target_private2") - - Prompt( - """ -你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中： -{style_habbits} -请你根据情景使用以下，不要盲目使用,不要生硬使用，而是结合到表达中： -{grammar_habbits} - -{memory_prompt} -{relation_prompt} -{prompt_info} -{chat_target} -现在时间是：{now_time} -{chat_talking_prompt} -现在"{sender_name}"说的:{message_txt}。引起了你的注意，你想要在群里发言或者回复这条消息。\n -你的网名叫{bot_name}，有人也叫你{bot_other_names}，{prompt_personality}。 - -{action_descriptions}你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，请你给出回复 -尽量简短一些。请注意把握聊天内容。 -请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景。 -{keywords_reaction_prompt} -请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容。 -{moderation_prompt} -不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""", - "reasoning_prompt_main", - ) - - Prompt( - "你回忆起：{related_memory_info}。\n以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n", - "memory_prompt", - ) - - Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt") - - Prompt( - """ -你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中： -{style_habbits} -请你根据情景使用以下句法，不要盲目使用,不要生硬使用，而是结合到表达中： -{grammar_habbits} -{memory_prompt} -{prompt_info} -你正在和 {sender_name} 聊天。 -{relation_prompt} -你们之前的聊天记录如下： -{chat_talking_prompt} -现在 {sender_name} 说的: {message_txt} 引起了你的注意，针对这条消息回复他。 -你的网名叫{bot_name}，{sender_name}也叫你{bot_other_names}，{prompt_personality}。 -{action_descriptions}你正在和 {sender_name} 聊天, 现在请你读读你们之前的聊天记录，给出回复。量简短一些。请注意把握聊天内容。 -{keywords_reaction_prompt} -{moderation_prompt} -请说中文。不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""", - "reasoning_prompt_private_main", # New template for private CHAT chat - ) - - -class PromptBuilder: - def __init__(self): - self.prompt_built = "" - self.activate_messages = "" - - async def build_prompt_normal( - self, - chat_stream, - message_txt: str, - sender_name: str = "某人", - enable_planner: bool = False, - available_actions=None, - ) -> str: - person_info_manager = get_person_info_manager() - bot_person_id = person_info_manager.get_person_id("system", "bot_id") - - short_impression = await person_info_manager.get_value(bot_person_id, "short_impression") - - # 解析字符串形式的Python列表 - try: - if isinstance(short_impression, str) and short_impression.strip(): - short_impression = ast.literal_eval(short_impression) - elif not short_impression: - logger.warning("short_impression为空，使用默认值") - short_impression = ["友好活泼", "人类"] - except (ValueError, SyntaxError) as e: - logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}") - short_impression = ["友好活泼", "人类"] - - # 确保short_impression是列表格式且有足够的元素 - if not isinstance(short_impression, list) or len(short_impression) < 2: - logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值") - short_impression = ["友好活泼", "人类"] - - personality = short_impression[0] - identity = short_impression[1] - prompt_personality = personality + "，" + identity - - is_group_chat = bool(chat_stream.group_info) - - who_chat_in_group = [] - if is_group_chat: - who_chat_in_group = get_recent_group_speaker( - chat_stream.stream_id, - (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None, - limit=global_config.normal_chat.max_context_size, - ) - who_chat_in_group.append( - (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname) - ) - - relation_prompt = "" - if global_config.relationship.enable_relationship: - for person in who_chat_in_group: - relationship_manager = get_relationship_manager() - relation_prompt += f"{await relationship_manager.build_relationship_info(person)}\n" - - mood_prompt = mood_manager.get_mood_prompt() - - memory_prompt = "" - if global_config.memory.enable_memory: - related_memory = await hippocampus_manager.get_memory_from_text( - text=message_txt, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False - ) - - related_memory_info = "" - if related_memory: - for memory in related_memory: - related_memory_info += memory[1] - memory_prompt = await global_prompt_manager.format_prompt( - "memory_prompt", related_memory_info=related_memory_info - ) - - message_list_before_now = get_raw_msg_before_timestamp_with_chat( - chat_id=chat_stream.stream_id, - timestamp=time.time(), - limit=global_config.focus_chat.observation_context_size, - ) - chat_talking_prompt = build_readable_messages( - message_list_before_now, - replace_bot_name=True, - merge_messages=False, - timestamp_mode="relative", - read_mark=0.0, - show_actions=True, - ) - - message_list_before_now_half = get_raw_msg_before_timestamp_with_chat( - chat_id=chat_stream.stream_id, - timestamp=time.time(), - limit=int(global_config.focus_chat.observation_context_size * 0.5), - ) - chat_talking_prompt_half = build_readable_messages( - message_list_before_now_half, - replace_bot_name=True, - merge_messages=False, - timestamp_mode="relative", - read_mark=0.0, - show_actions=True, - ) - - expressions = await expression_selector.select_suitable_expressions_llm( - chat_stream.stream_id, chat_talking_prompt_half, max_num=8, min_num=3 - ) - style_habbits = [] - grammar_habbits = [] - if expressions: - for expr in expressions: - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - expr_type = expr.get("type", "style") - if expr_type == "grammar": - grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") - else: - style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") - else: - logger.debug("没有从处理器获得表达方式，将使用空的表达方式") - - style_habbits_str = "\n".join(style_habbits) - grammar_habbits_str = "\n".join(grammar_habbits) - - # 关键词检测与反应 - keywords_reaction_prompt = "" - try: - # 处理关键词规则 - for rule in global_config.keyword_reaction.keyword_rules: - if any(keyword in message_txt for keyword in rule.keywords): - logger.info(f"检测到关键词规则：{rule.keywords}，触发反应：{rule.reaction}") - keywords_reaction_prompt += f"{rule.reaction}，" - - # 处理正则表达式规则 - for rule in global_config.keyword_reaction.regex_rules: - for pattern_str in rule.regex: - try: - pattern = re.compile(pattern_str) - if result := pattern.search(message_txt): - reaction = rule.reaction - for name, content in result.groupdict().items(): - reaction = reaction.replace(f"[{name}]", content) - logger.info(f"匹配到正则表达式：{pattern_str}，触发反应：{reaction}") - keywords_reaction_prompt += reaction + "，" - break - except re.error as e: - logger.error(f"正则表达式编译错误: {pattern_str}, 错误信息: {str(e)}") - continue - except Exception as e: - logger.error(f"关键词检测与反应时发生异常: {str(e)}", exc_info=True) - - moderation_prompt_block = ( - "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。" - ) - - # 构建action描述 (如果启用planner) - action_descriptions = "" - # logger.debug(f"Enable planner {enable_planner}, available actions: {available_actions}") - if enable_planner and available_actions: - action_descriptions = "你有以下的动作能力，但执行这些动作不由你决定，由另外一个模型同步决定，因此你只需要知道有如下能力即可：\n" - for action_name, action_info in available_actions.items(): - action_description = action_info.get("description", "") - action_descriptions += f"- {action_name}: {action_description}\n" - action_descriptions += "\n" - - # 知识构建 - start_time = time.time() - prompt_info = await self.get_prompt_info(message_txt, threshold=0.38) - if prompt_info: - prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info) - - end_time = time.time() - logger.debug(f"知识检索耗时: {(end_time - start_time):.3f}秒") - - logger.debug("开始构建 normal prompt") - - now_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) - - # --- Choose template and format based on chat type --- - if is_group_chat: - template_name = "reasoning_prompt_main" - effective_sender_name = sender_name - chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1") - chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2") - - prompt = await global_prompt_manager.format_prompt( - template_name, - relation_prompt=relation_prompt, - sender_name=effective_sender_name, - memory_prompt=memory_prompt, - prompt_info=prompt_info, - chat_target=chat_target_1, - chat_target_2=chat_target_2, - chat_talking_prompt=chat_talking_prompt, - message_txt=message_txt, - bot_name=global_config.bot.nickname, - bot_other_names="/".join(global_config.bot.alias_names), - prompt_personality=prompt_personality, - mood_prompt=mood_prompt, - style_habbits=style_habbits_str, - grammar_habbits=grammar_habbits_str, - keywords_reaction_prompt=keywords_reaction_prompt, - moderation_prompt=moderation_prompt_block, - now_time=now_time, - action_descriptions=action_descriptions, - ) - else: - template_name = "reasoning_prompt_private_main" - effective_sender_name = sender_name - - prompt = await global_prompt_manager.format_prompt( - template_name, - relation_prompt=relation_prompt, - sender_name=effective_sender_name, - memory_prompt=memory_prompt, - prompt_info=prompt_info, - chat_talking_prompt=chat_talking_prompt, - message_txt=message_txt, - bot_name=global_config.bot.nickname, - bot_other_names="/".join(global_config.bot.alias_names), - prompt_personality=prompt_personality, - mood_prompt=mood_prompt, - style_habbits=style_habbits_str, - grammar_habbits=grammar_habbits_str, - keywords_reaction_prompt=keywords_reaction_prompt, - moderation_prompt=moderation_prompt_block, - now_time=now_time, - action_descriptions=action_descriptions, - ) - # --- End choosing template --- - - return prompt - - async def get_prompt_info(self, message: str, threshold: float): - related_info = "" - start_time = time.time() - - logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") - # 从LPMM知识库获取知识 - try: - found_knowledge_from_lpmm = qa_manager.get_knowledge(message) - - end_time = time.time() - if found_knowledge_from_lpmm is not None: - logger.debug( - f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}" - ) - related_info += found_knowledge_from_lpmm - logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒") - logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}") - return related_info - else: - logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...") - return "未检索到知识" - except Exception as e: - logger.error(f"获取知识库内容时发生异常: {str(e)}") - return "未检索到知识" - - -def weighted_sample_no_replacement(items, weights, k) -> list: - """ - 加权且不放回地随机抽取k个元素。 - - 参数： - items: 待抽取的元素列表 - weights: 每个元素对应的权重（与items等长，且为正数） - k: 需要抽取的元素个数 - 返回： - selected: 按权重加权且不重复抽取的k个元素组成的列表 - - 如果 items 中的元素不足 k 个，就只会返回所有可用的元素 - - 实现思路： - 每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。 - 这样保证了： - 1. count越大被选中概率越高 - 2. 不会重复选中同一个元素 - """ - selected = [] - pool = list(zip(items, weights)) - for _ in range(min(k, len(pool))): - total = sum(w for _, w in pool) - r = random.uniform(0, total) - upto = 0 - for idx, (item, weight) in enumerate(pool): - upto += weight - if upto >= r: - selected.append(item) - pool.pop(idx) - break - return selected - - -init_prompt() -prompt_builder = PromptBuilder() diff --git a/src/chat/normal_chat/priority_manager.py b/src/chat/normal_chat/priority_manager.py new file mode 100644 index 000000000..9e1ef76c2 --- /dev/null +++ b/src/chat/normal_chat/priority_manager.py @@ -0,0 +1,108 @@ +import time +import heapq +import math +from typing import List, Dict, Optional +from ..message_receive.message import MessageRecv +from src.common.logger import get_logger + +logger = get_logger("normal_chat") + + +class PrioritizedMessage: + """带有优先级的消息对象""" + + def __init__(self, message: MessageRecv, interest_scores: List[float], is_vip: bool = False): + self.message = message + self.arrival_time = time.time() + self.interest_scores = interest_scores + self.is_vip = is_vip + self.priority = self.calculate_priority() + + def calculate_priority(self, decay_rate: float = 0.01) -> float: + """ + 计算优先级分数。 + 优先级 = 兴趣分 * exp(-衰减率 * 消息年龄) + """ + age = time.time() - self.arrival_time + decay_factor = math.exp(-decay_rate * age) + priority = sum(self.interest_scores) + decay_factor + return priority + + def __lt__(self, other: "PrioritizedMessage") -> bool: + """用于堆排序的比较函数，我们想要一个最大堆，所以用 >""" + return self.priority > other.priority + + +class PriorityManager: + """ + 管理消息队列，根据优先级选择消息进行处理。 + """ + + def __init__(self, interest_dict: Dict[str, float], normal_queue_max_size: int = 5): + self.vip_queue: List[PrioritizedMessage] = [] # VIP 消息队列 (最大堆) + self.normal_queue: List[PrioritizedMessage] = [] # 普通消息队列 (最大堆) + self.interest_dict = interest_dict if interest_dict is not None else {} + self.normal_queue_max_size = normal_queue_max_size + + def _get_interest_score(self, user_id: str) -> float: + """获取用户的兴趣分，默认为1.0""" + return self.interest_dict.get("interests", {}).get(user_id, 1.0) + + def add_message(self, message: MessageRecv, interest_score: Optional[float] = None): + """ + 添加新消息到合适的队列中。 + """ + user_id = message.message_info.user_info.user_id + is_vip = message.priority_info.get("message_type") == "vip" if message.priority_info else False + message_priority = message.priority_info.get("message_priority", 0.0) if message.priority_info else 0.0 + + p_message = PrioritizedMessage(message, [interest_score, message_priority], is_vip) + + if is_vip: + heapq.heappush(self.vip_queue, p_message) + logger.debug(f"消息来自VIP用户 {user_id}, 已添加到VIP队列. 当前VIP队列长度: {len(self.vip_queue)}") + else: + if len(self.normal_queue) >= self.normal_queue_max_size: + # 如果队列已满，只在消息优先级高于最低优先级消息时才添加 + if p_message.priority > self.normal_queue[0].priority: + heapq.heapreplace(self.normal_queue, p_message) + logger.debug(f"普通队列已满，但新消息优先级更高，已替换. 用户: {user_id}") + else: + logger.debug(f"普通队列已满且新消息优先级较低，已忽略. 用户: {user_id}") + else: + heapq.heappush(self.normal_queue, p_message) + logger.debug( + f"消息来自普通用户 {user_id}, 已添加到普通队列. 当前普通队列长度: {len(self.normal_queue)}" + ) + + def get_highest_priority_message(self) -> Optional[MessageRecv]: + """ + 从VIP和普通队列中获取当前最高优先级的消息。 + """ + # 更新所有消息的优先级 + for p_msg in self.vip_queue: + p_msg.priority = p_msg.calculate_priority() + for p_msg in self.normal_queue: + p_msg.priority = p_msg.calculate_priority() + + # 重建堆 + heapq.heapify(self.vip_queue) + heapq.heapify(self.normal_queue) + + vip_msg = self.vip_queue[0] if self.vip_queue else None + normal_msg = self.normal_queue[0] if self.normal_queue else None + + if vip_msg: + return heapq.heappop(self.vip_queue).message + elif normal_msg: + return heapq.heappop(self.normal_queue).message + else: + return None + + def is_empty(self) -> bool: + """检查所有队列是否为空""" + return not self.vip_queue and not self.normal_queue + + def get_queue_status(self) -> str: + """获取队列状态信息""" + return f"VIP队列: {len(self.vip_queue)}, 普通队列: {len(self.normal_queue)}" diff --git a/src/chat/normal_chat/willing/mode_classical.py b/src/chat/normal_chat/willing/mode_classical.py index 3ffe23c46..0b296bbf4 100644 --- a/src/chat/normal_chat/willing/mode_classical.py +++ b/src/chat/normal_chat/willing/mode_classical.py @@ -33,28 +33,10 @@ class ClassicalWillingManager(BaseWillingManager): if willing_info.is_mentioned_bot: current_willing += 1 if current_willing < 1.0 else 0.05 - is_emoji_not_reply = False - if willing_info.is_emoji: - if global_config.normal_chat.emoji_response_penalty != 0: - current_willing *= global_config.normal_chat.emoji_response_penalty - else: - is_emoji_not_reply = True - - # 处理picid格式消息，直接不回复 - is_picid_not_reply = False - if willing_info.is_picid: - is_picid_not_reply = True - self.chat_reply_willing[chat_id] = min(current_willing, 3.0) reply_probability = min(max((current_willing - 0.5), 0.01) * 2, 1) - if is_emoji_not_reply: - reply_probability = 0 - - if is_picid_not_reply: - reply_probability = 0 - return reply_probability async def before_generate_reply_handle(self, message_id): @@ -71,8 +53,5 @@ class ClassicalWillingManager(BaseWillingManager): if current_willing < 1: self.chat_reply_willing[chat_id] = min(1.0, current_willing + 0.4) - async def bombing_buffer_message_handle(self, message_id): - return await super().bombing_buffer_message_handle(message_id) - async def not_reply_handle(self, message_id): return await super().not_reply_handle(message_id) diff --git a/src/chat/normal_chat/willing/mode_custom.py b/src/chat/normal_chat/willing/mode_custom.py index 4b2e8f3c3..36334df43 100644 --- a/src/chat/normal_chat/willing/mode_custom.py +++ b/src/chat/normal_chat/willing/mode_custom.py @@ -17,8 +17,5 @@ class CustomWillingManager(BaseWillingManager): async def get_reply_probability(self, message_id: str): pass - async def bombing_buffer_message_handle(self, message_id: str): - pass - def __init__(self): super().__init__() diff --git a/src/chat/normal_chat/willing/mode_mxp.py b/src/chat/normal_chat/willing/mode_mxp.py index 03651d080..7b9e55568 100644 --- a/src/chat/normal_chat/willing/mode_mxp.py +++ b/src/chat/normal_chat/willing/mode_mxp.py @@ -19,7 +19,6 @@ Mxp 模式：梦溪畔独家赞助 下下策是询问一个菜鸟（@梦溪畔） """ -from src.config.config import global_config from .willing_manager import BaseWillingManager from typing import Dict import asyncio @@ -173,22 +172,10 @@ class MxpWillingManager(BaseWillingManager): probability = self._willing_to_probability(current_willing) - if w_info.is_emoji: - probability *= global_config.normal_chat.emoji_response_penalty - - if w_info.is_picid: - probability = 0 # picid格式消息直接不回复 - self.temporary_willing = current_willing return probability - async def bombing_buffer_message_handle(self, message_id: str): - """炸飞消息处理""" - async with self.lock: - w_info = self.ongoing_messages[message_id] - self.chat_person_reply_willing[w_info.chat_id][w_info.person_id] += 0.1 - async def _return_to_basic_willing(self): """使每个人的意愿恢复到chat基础意愿""" while True: diff --git a/src/chat/normal_chat/willing/willing_manager.py b/src/chat/normal_chat/willing/willing_manager.py index 47c6bfd0f..0fa701f94 100644 --- a/src/chat/normal_chat/willing/willing_manager.py +++ b/src/chat/normal_chat/willing/willing_manager.py @@ -20,7 +20,6 @@ before_generate_reply_handle 确定要回复后，在生成回复前的处理 after_generate_reply_handle 确定要回复后，在生成回复后的处理 not_reply_handle 确定不回复后的处理 get_reply_probability 获取回复概率 -bombing_buffer_message_handle 缓冲器炸飞消息后的处理 get_variable_parameters 暂不确定 set_variable_parameters 暂不确定 以下2个方法根据你的实现可以做调整： @@ -137,11 +136,6 @@ class BaseWillingManager(ABC): """抽象方法：获取回复概率""" raise NotImplementedError - @abstractmethod - async def bombing_buffer_message_handle(self, message_id: str): - """抽象方法：炸飞消息处理""" - pass - async def get_willing(self, chat_id: str): """获取指定聊天流的回复意愿""" async with self.lock: diff --git a/src/chat/replyer/default_generator.py b/src/chat/replyer/default_generator.py index bf247e425..da9d9a584 100644 --- a/src/chat/replyer/default_generator.py +++ b/src/chat/replyer/default_generator.py @@ -10,128 +10,135 @@ from src.llm_models.utils_model import LLMRequest from src.config.config import global_config from src.chat.utils.timer_calculator import Timer # <--- Import Timer from src.chat.focus_chat.heartFC_sender import HeartFCSender -from src.chat.utils.utils import process_llm_response from src.chat.heart_flow.utils_chat import get_chat_type_and_target_info from src.chat.message_receive.chat_stream import ChatStream from src.chat.focus_chat.hfc_utils import parse_thinking_id_to_timestamp from src.chat.utils.prompt_builder import Prompt, global_prompt_manager from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat -from src.chat.express.exprssion_learner import get_expression_learner import time +import asyncio +from src.chat.express.expression_selector import expression_selector +from src.manager.mood_manager import mood_manager +from src.person_info.relationship_fetcher import relationship_fetcher_manager import random import ast from src.person_info.person_info import get_person_info_manager from datetime import datetime import re +from src.chat.knowledge.knowledge_lib import qa_manager +from src.chat.focus_chat.memory_activator import MemoryActivator +from src.tools.tool_executor import ToolExecutor logger = get_logger("replyer") def init_prompt(): + Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1") + Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1") + Prompt("在群里聊天", "chat_target_group2") + Prompt("和{sender_name}聊天", "chat_target_private2") + Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt") + Prompt( """ {expression_habits_block} -{structured_info_block} +{tool_info_block} +{knowledge_prompt} {memory_block} {relation_info_block} {extra_info_block} -{time_block} + {chat_target} +{time_block} {chat_info} {reply_target_block} {identity} -你需要使用合适的语言习惯和句法，参考聊天内容，组织一条日常且口语化的回复。注意不要复读你说过的话。 -{config_expression_style}。回复不要浮夸，不要用夸张修辞，平淡一些。 +{action_descriptions} +你正在{chat_target_2},现在请你读读之前的聊天记录，{mood_prompt}，请你给出回复 +{config_expression_style}。 +请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景，注意不要复读你说过的话。 {keywords_reaction_prompt} -请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。 -不要浮夸，不要夸张修辞，请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出一条回复就好。 -现在，你说： -""", +请注意不要输出多余内容(包括前后缀，冒号和引号，at或 @等 )。只输出回复内容。 +{moderation_prompt} +不要浮夸，不要夸张修辞，不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容""", "default_generator_prompt", ) Prompt( """ {expression_habits_block} -{structured_info_block} -{memory_block} {relation_info_block} -{extra_info_block} + +{chat_target} {time_block} -{chat_target} {chat_info} -现在"{sender_name}"说:{target_message}。你想要回复对方的这条消息。 -{identity}， -你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。注意不要复读你说过的话。 +{identity} -{config_expression_style}。回复不要浮夸，不要用夸张修辞，平淡一些。 -{keywords_reaction_prompt} -请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。 -不要浮夸，不要夸张修辞，请注意不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出一条回复就好。 -现在，你说： -""", - "default_generator_private_prompt", - ) - - Prompt( - """ -你可以参考你的以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中： -{style_habbits} - -你现在正在群里聊天，以下是群里正在进行的聊天内容： -{chat_info} - -以上是聊天内容，你需要了解聊天记录中的内容 - -{chat_target} -你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{sender_name}"说的"{target_message}"引起了你的注意，对这句话，你想表达：{raw_reply},原因是：{reason}。你现在要思考怎么回复 +你正在{chat_target_2},{reply_target_block} +对这句话，你想表达，原句：{raw_reply},原因是：{reason}。你现在要思考怎么组织回复 你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。请你修改你想表达的原句，符合你的表达风格和语言习惯 -请你根据情景使用以下句法： -{grammar_habbits} {config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。 +{keywords_reaction_prompt} +{moderation_prompt} 不要浮夸，不要夸张修辞，平淡且不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )，只输出一条回复就好。 现在，你说： """, "default_expressor_prompt", ) - Prompt( - """ -你可以参考以下的语言习惯，如果情景合适就使用，不要盲目使用,不要生硬使用，而是结合到表达中： -{style_habbits} - -你现在正在群里聊天，以下是群里正在进行的聊天内容： -{chat_info} - -以上是聊天内容，你需要了解聊天记录中的内容 - -{chat_target} -你的名字是{bot_name}，{prompt_personality}，在这聊天中，"{sender_name}"说的"{target_message}"引起了你的注意，对这句话，你想表达：{raw_reply},原因是：{reason}。你现在要思考怎么回复 -你需要使用合适的语法和句法，参考聊天内容，组织一条日常且口语化的回复。 -请你根据情景使用以下句法： -{grammar_habbits} -{config_expression_style}，你可以完全重组回复，保留最基本的表达含义就好，但重组后保持语意通顺。 -不要浮夸，不要夸张修辞，平淡且不要输出多余内容(包括前后缀，冒号和引号，括号，表情包，at或 @等 )，只输出一条回复就好。 -现在，你说： -""", - "default_expressor_private_prompt", # New template for private FOCUSED chat - ) - class DefaultReplyer: - def __init__(self, chat_stream: ChatStream): + def __init__( + self, + chat_stream: ChatStream, + enable_tool: bool = False, + model_configs: Optional[List[Dict[str, Any]]] = None, + request_type: str = "focus.replyer", + ): self.log_prefix = "replyer" - # TODO: API-Adapter修改标记 - self.express_model = LLMRequest( - model=global_config.model.replyer_1, - request_type="focus.replyer", - ) - self.heart_fc_sender = HeartFCSender() + self.request_type = request_type + + self.enable_tool = enable_tool + + if model_configs: + self.express_model_configs = model_configs + else: + # 当未提供配置时，使用默认配置并赋予默认权重 + + model_config_1 = global_config.model.replyer_1.copy() + model_config_2 = global_config.model.replyer_2.copy() + prob_first = global_config.chat.replyer_random_probability + + model_config_1["weight"] = prob_first + model_config_2["weight"] = 1.0 - prob_first + + self.express_model_configs = [model_config_1, model_config_2] + + if not self.express_model_configs: + logger.warning("未找到有效的模型配置，回复生成可能会失败。") + # 提供一个最终的回退，以防止在空列表上调用 random.choice + fallback_config = global_config.model.replyer_1.copy() + fallback_config.setdefault("weight", 1.0) + self.express_model_configs = [fallback_config] self.chat_stream = chat_stream self.is_group_chat, self.chat_target_info = get_chat_type_and_target_info(self.chat_stream.stream_id) + self.heart_fc_sender = HeartFCSender() + self.memory_activator = MemoryActivator() + self.tool_executor = ToolExecutor(chat_id=self.chat_stream.stream_id, enable_cache=True, cache_ttl=3) + + def _select_weighted_model_config(self) -> Dict[str, Any]: + """使用加权随机选择来挑选一个模型配置""" + configs = self.express_model_configs + # 提取权重，如果模型配置中没有'weight'键，则默认为1.0 + weights = [config.get("weight", 1.0) for config in configs] + + # random.choices 返回一个列表，我们取第一个元素 + selected_config = random.choices(population=configs, weights=weights, k=1)[0] + return selected_config + async def _create_thinking_message(self, anchor_message: Optional[MessageRecv], thinking_id: str): """创建思考消息 (尝试锚定到 anchor_message)""" if not anchor_message or not anchor_message.chat_stream: @@ -161,17 +168,36 @@ class DefaultReplyer: async def generate_reply_with_context( self, - reply_data: Dict[str, Any], - ) -> Tuple[bool, Optional[List[str]]]: + reply_data: Dict[str, Any] = None, + reply_to: str = "", + relation_info: str = "", + extra_info: str = "", + available_actions: List[str] = None, + ) -> Tuple[bool, Optional[str]]: """ 回复器 (Replier): 核心逻辑，负责生成回复文本。 (已整合原 HeartFCGenerator 的功能) """ + if available_actions is None: + available_actions = [] + if reply_data is None: + reply_data = {} try: + if not reply_data: + reply_data = { + "reply_to": reply_to, + "relation_info": relation_info, + "extra_info": extra_info, + } + for key, value in reply_data.items(): + if not value: + logger.info(f"{self.log_prefix} 回复数据跳过{key}，生成回复时将忽略。") + # 3. 构建 Prompt with Timer("构建Prompt", {}): # 内部计时器，可选保留 prompt = await self.build_prompt_reply_context( reply_data=reply_data, # 传递action_data + available_actions=available_actions, ) # 4. 调用 LLM 生成回复 @@ -181,8 +207,19 @@ class DefaultReplyer: try: with Timer("LLM生成", {}): # 内部计时器，可选保留 + # 加权随机选择一个模型配置 + selected_model_config = self._select_weighted_model_config() + logger.info( + f"{self.log_prefix} 使用模型配置: {selected_model_config.get('name', 'N/A')} (权重: {selected_model_config.get('weight', 1.0)})" + ) + + express_model = LLMRequest( + model=selected_model_config, + request_type=self.request_type, + ) + logger.info(f"{self.log_prefix}Prompt:\n{prompt}\n") - content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt) + content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt) logger.info(f"最终回复: {content}") @@ -191,22 +228,7 @@ class DefaultReplyer: logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}") return False, None # LLM 调用失败则无法生成回复 - processed_response = process_llm_response(content) - - # 5. 处理 LLM 响应 - if not content: - logger.warning(f"{self.log_prefix}LLM 生成了空内容。") - return False, None - if not processed_response: - logger.warning(f"{self.log_prefix}处理后的回复为空。") - return False, None - - reply_set = [] - for str in processed_response: - reply_seg = ("text", str) - reply_set.append(reply_seg) - - return True, reply_set + return True, content, prompt except Exception as e: logger.error(f"{self.log_prefix}回复生成意外失败: {e}") @@ -216,20 +238,24 @@ class DefaultReplyer: async def rewrite_reply_with_context( self, reply_data: Dict[str, Any], - ) -> Tuple[bool, Optional[List[str]]]: + raw_reply: str = "", + reason: str = "", + reply_to: str = "", + relation_info: str = "", + ) -> Tuple[bool, Optional[str]]: """ 表达器 (Expressor): 核心逻辑，负责生成回复文本。 """ try: - reply_to = reply_data.get("reply_to", "") - raw_reply = reply_data.get("raw_reply", "") - reason = reply_data.get("reason", "") + if not reply_data: + reply_data = { + "reply_to": reply_to, + "relation_info": relation_info, + } with Timer("构建Prompt", {}): # 内部计时器，可选保留 prompt = await self.build_prompt_rewrite_context( - raw_reply=raw_reply, - reason=reason, - reply_to=reply_to, + reply_data=reply_data, ) content = None @@ -241,89 +267,67 @@ class DefaultReplyer: try: with Timer("LLM生成", {}): # 内部计时器，可选保留 - # TODO: API-Adapter修改标记 - content, (reasoning_content, model_name) = await self.express_model.generate_response_async(prompt) + # 加权随机选择一个模型配置 + selected_model_config = self._select_weighted_model_config() + logger.info( + f"{self.log_prefix} 使用模型配置进行重写: {selected_model_config.get('model_name', 'N/A')} (权重: {selected_model_config.get('weight', 1.0)})" + ) - logger.info(f"想要表达：{raw_reply}||理由：{reason}") - logger.info(f"最终回复: {content}\n") + express_model = LLMRequest( + model=selected_model_config, + request_type=self.request_type, + ) + + content, (reasoning_content, model_name) = await express_model.generate_response_async(prompt) + + logger.info(f"想要表达：{raw_reply}||理由：{reason}||生成回复: {content}\n") except Exception as llm_e: # 精简报错信息 logger.error(f"{self.log_prefix}LLM 生成失败: {llm_e}") return False, None # LLM 调用失败则无法生成回复 - processed_response = process_llm_response(content) - - # 5. 处理 LLM 响应 - if not content: - logger.warning(f"{self.log_prefix}LLM 生成了空内容。") - return False, None - if not processed_response: - logger.warning(f"{self.log_prefix}处理后的回复为空。") - return False, None - - reply_set = [] - for str in processed_response: - reply_seg = ("text", str) - reply_set.append(reply_seg) - - return True, reply_set + return True, content except Exception as e: logger.error(f"{self.log_prefix}回复生成意外失败: {e}") traceback.print_exc() return False, None - async def build_prompt_reply_context( - self, - reply_data=None, - ) -> str: - chat_stream = self.chat_stream + async def build_relation_info(self, reply_data=None, chat_history=None): + if not global_config.relationship.enable_relationship: + return "" + + relationship_fetcher = relationship_fetcher_manager.get_fetcher(self.chat_stream.stream_id) + if not reply_data: + return "" + reply_to = reply_data.get("reply_to", "") + sender, text = self._parse_reply_target(reply_to) + if not sender or not text: + return "" + + # 获取用户ID person_info_manager = get_person_info_manager() - bot_person_id = person_info_manager.get_person_id("system", "bot_id") + person_id = person_info_manager.get_person_id_by_person_name(sender) + if not person_id: + logger.warning(f"{self.log_prefix} 未找到用户 {sender} 的ID，跳过信息提取") + return None - is_group_chat = bool(chat_stream.group_info) + relation_info = await relationship_fetcher.build_relation_info(person_id, text, chat_history) + return relation_info - self_info_block = reply_data.get("self_info_block", "") - structured_info = reply_data.get("structured_info", "") - relation_info_block = reply_data.get("relation_info_block", "") - reply_to = reply_data.get("reply_to", "none") - memory_block = reply_data.get("memory_block", "") - - # 优先使用 extra_info_block，没有则用 extra_info - extra_info_block = reply_data.get("extra_info_block", "") or reply_data.get("extra_info", "") - - sender = "" - target = "" - if ":" in reply_to or "：" in reply_to: - # 使用正则表达式匹配中文或英文冒号 - parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1) - if len(parts) == 2: - sender = parts[0].strip() - target = parts[1].strip() - - message_list_before_now = get_raw_msg_before_timestamp_with_chat( - chat_id=chat_stream.stream_id, - timestamp=time.time(), - limit=global_config.focus_chat.observation_context_size, - ) - # print(f"message_list_before_now: {message_list_before_now}") - chat_talking_prompt = build_readable_messages( - message_list_before_now, - replace_bot_name=True, - merge_messages=False, - timestamp_mode="normal_no_YMD", - read_mark=0.0, - truncate=True, - show_actions=True, - ) - # print(f"chat_talking_prompt: {chat_talking_prompt}") + async def build_expression_habits(self, chat_history, target): + if not global_config.expression.enable_expression: + return "" style_habbits = [] grammar_habbits = [] # 使用从处理器传来的选中表达方式 - selected_expressions = reply_data.get("selected_expressions", []) if reply_data else [] + # LLM模式：调用LLM选择5-10个，然后随机选5个 + selected_expressions = await expression_selector.select_suitable_expressions_llm( + self.chat_stream.stream_id, chat_history, max_num=8, min_num=2, target_message=target + ) if selected_expressions: logger.info(f"{self.log_prefix} 使用处理器选中的{len(selected_expressions)}个表达方式") @@ -348,16 +352,85 @@ class DefaultReplyer: if grammar_habbits_str.strip(): expression_habits_block += f"请你根据情景使用以下句法：\n{grammar_habbits_str}\n" - if structured_info: - structured_info_block = f"以下是一些额外的信息，现在请你阅读以下内容，进行决策\n{structured_info}\n以上是一些额外的信息，现在请你阅读以下内容，进行决策" - else: - structured_info_block = "" + return expression_habits_block - if extra_info_block: - extra_info_block = f"以下是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策" - else: - extra_info_block = "" + async def build_memory_block(self, chat_history, target): + if not global_config.memory.enable_memory: + return "" + running_memorys = await self.memory_activator.activate_memory_with_chat_history( + target_message=target, chat_history_prompt=chat_history + ) + + if running_memorys: + memory_str = "以下是当前在聊天中，你回忆起的记忆：\n" + for running_memory in running_memorys: + memory_str += f"- {running_memory['content']}\n" + memory_block = memory_str + logger.info(f"{self.log_prefix} 添加了 {len(running_memorys)} 个激活的记忆到prompt") + else: + memory_block = "" + + return memory_block + + async def build_tool_info(self, reply_data=None, chat_history=None): + """构建工具信息块 + + Args: + reply_data: 回复数据，包含要回复的消息内容 + chat_history: 聊天历史 + + Returns: + str: 工具信息字符串 + """ + + if not reply_data: + return "" + + reply_to = reply_data.get("reply_to", "") + sender, text = self._parse_reply_target(reply_to) + + if not text: + return "" + + try: + # 使用工具执行器获取信息 + tool_results = await self.tool_executor.execute_from_chat_message( + sender=sender, target_message=text, chat_history=chat_history, return_details=False + ) + + if tool_results: + tool_info_str = "以下是你通过工具获取到的实时信息：\n" + for tool_result in tool_results: + tool_name = tool_result.get("tool_name", "unknown") + content = tool_result.get("content", "") + result_type = tool_result.get("type", "info") + + tool_info_str += f"- 【{tool_name}】{result_type}: {content}\n" + + tool_info_str += "以上是你获取到的实时信息，请在回复时参考这些信息。" + logger.info(f"{self.log_prefix} 获取到 {len(tool_results)} 个工具结果") + return tool_info_str + else: + logger.debug(f"{self.log_prefix} 未获取到任何工具结果") + return "" + + except Exception as e: + logger.error(f"{self.log_prefix} 工具信息获取失败: {e}") + return "" + + def _parse_reply_target(self, target_message: str) -> tuple: + sender = "" + target = "" + if ":" in target_message or "：" in target_message: + # 使用正则表达式匹配中文或英文冒号 + parts = re.split(pattern=r"[:：]", string=target_message, maxsplit=1) + if len(parts) == 2: + sender = parts[0].strip() + target = parts[1].strip() + return sender, target + + async def build_keywords_reaction_prompt(self, target): # 关键词检测与反应 keywords_reaction_prompt = "" try: @@ -385,6 +458,95 @@ class DefaultReplyer: except Exception as e: logger.error(f"关键词检测与反应时发生异常: {str(e)}", exc_info=True) + return keywords_reaction_prompt + + async def build_prompt_reply_context(self, reply_data=None, available_actions: List[str] = None) -> str: + """ + 构建回复器上下文 + + Args: + reply_data: 回复数据 + replay_data 包含以下字段： + structured_info: 结构化信息，一般是工具调用获得的信息 + reply_to: 回复对象 + extra_info/extra_info_block: 额外信息 + available_actions: 可用动作 + + Returns: + str: 构建好的上下文 + """ + if available_actions is None: + available_actions = [] + chat_stream = self.chat_stream + chat_id = chat_stream.stream_id + person_info_manager = get_person_info_manager() + bot_person_id = person_info_manager.get_person_id("system", "bot_id") + is_group_chat = bool(chat_stream.group_info) + reply_to = reply_data.get("reply_to", "none") + extra_info_block = reply_data.get("extra_info", "") or reply_data.get("extra_info_block", "") + + sender, target = self._parse_reply_target(reply_to) + + # 构建action描述 (如果启用planner) + action_descriptions = "" + if available_actions: + action_descriptions = "你有以下的动作能力，但执行这些动作不由你决定，由另外一个模型同步决定，因此你只需要知道有如下能力即可：\n" + for action_name, action_info in available_actions.items(): + action_description = action_info.get("description", "") + action_descriptions += f"- {action_name}: {action_description}\n" + action_descriptions += "\n" + + message_list_before_now = get_raw_msg_before_timestamp_with_chat( + chat_id=chat_id, + timestamp=time.time(), + limit=global_config.chat.max_context_size, + ) + chat_talking_prompt = build_readable_messages( + message_list_before_now, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="normal_no_YMD", + read_mark=0.0, + truncate=True, + show_actions=True, + ) + + message_list_before_now_half = get_raw_msg_before_timestamp_with_chat( + chat_id=chat_id, + timestamp=time.time(), + limit=int(global_config.chat.max_context_size * 0.5), + ) + chat_talking_prompt_half = build_readable_messages( + message_list_before_now_half, + replace_bot_name=True, + merge_messages=False, + timestamp_mode="relative", + read_mark=0.0, + show_actions=True, + ) + + # 并行执行四个构建任务 + expression_habits_block, relation_info, memory_block, tool_info = await asyncio.gather( + self.build_expression_habits(chat_talking_prompt_half, target), + self.build_relation_info(reply_data, chat_talking_prompt_half), + self.build_memory_block(chat_talking_prompt_half, target), + self.build_tool_info(reply_data, chat_talking_prompt_half), + ) + + keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target) + + if tool_info: + tool_info_block = ( + f"以下是你了解的额外信息信息，现在请你阅读以下内容，进行决策\n{tool_info}\n以上是一些额外的信息。" + ) + else: + tool_info_block = "" + + if extra_info_block: + extra_info_block = f"以下是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策\n{extra_info_block}\n以上是你在回复时需要参考的信息，现在请你阅读以下内容，进行决策" + else: + extra_info_block = "" + time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" # logger.debug("开始构建 focus prompt") @@ -404,7 +566,6 @@ class DefaultReplyer: except (ValueError, SyntaxError) as e: logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}") short_impression = ["友好活泼", "人类"] - # 确保short_impression是列表格式且有足够的元素 if not isinstance(short_impression, list) or len(short_impression) < 2: logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值") @@ -414,177 +575,201 @@ class DefaultReplyer: prompt_personality = personality + "，" + identity indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}：" - if sender: - reply_target_block = f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。" - elif target: - reply_target_block = f"现在{target}引起了你的注意，你想要在群里发言或者回复这条消息。" + moderation_prompt_block = ( + "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。" + ) + + if sender and target: + if is_group_chat: + if sender: + reply_target_block = ( + f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。" + ) + elif target: + reply_target_block = f"现在{target}引起了你的注意，你想要在群里发言或者回复这条消息。" + else: + reply_target_block = "现在，你想要在群里发言或者回复消息。" + else: # private chat + if sender: + reply_target_block = f"现在{sender}说的:{target}。引起了你的注意，针对这条消息回复。" + elif target: + reply_target_block = f"现在{target}引起了你的注意，针对这条消息回复。" + else: + reply_target_block = "现在，你想要回复。" else: - reply_target_block = "现在，你想要在群里发言或者回复消息。" + reply_target_block = "" - # --- Choose template based on chat type --- + mood_prompt = mood_manager.get_mood_prompt() + + prompt_info = await get_prompt_info(target, threshold=0.38) + if prompt_info: + prompt_info = await global_prompt_manager.format_prompt("knowledge_prompt", prompt_info=prompt_info) + + template_name = "default_generator_prompt" if is_group_chat: - template_name = "default_generator_prompt" - # Group specific formatting variables (already fetched or default) chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1") - # chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2") - - prompt = await global_prompt_manager.format_prompt( - template_name, - expression_habits_block=expression_habits_block, - chat_target=chat_target_1, - chat_info=chat_talking_prompt, - memory_block=memory_block, - structured_info_block=structured_info_block, - extra_info_block=extra_info_block, - relation_info_block=relation_info_block, - self_info_block=self_info_block, - time_block=time_block, - reply_target_block=reply_target_block, - keywords_reaction_prompt=keywords_reaction_prompt, - identity=indentify_block, - target_message=target, - sender_name=sender, - config_expression_style=global_config.expression.expression_style, - ) - else: # Private chat - template_name = "default_generator_private_prompt" - # 在私聊时获取对方的昵称信息 + chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2") + else: chat_target_name = "对方" if self.chat_target_info: chat_target_name = ( self.chat_target_info.get("person_name") or self.chat_target_info.get("user_nickname") or "对方" ) - chat_target_1 = f"你正在和 {chat_target_name} 聊天" - prompt = await global_prompt_manager.format_prompt( - template_name, - expression_habits_block=expression_habits_block, - chat_target=chat_target_1, - chat_info=chat_talking_prompt, - memory_block=memory_block, - structured_info_block=structured_info_block, - relation_info_block=relation_info_block, - extra_info_block=extra_info_block, - time_block=time_block, - keywords_reaction_prompt=keywords_reaction_prompt, - identity=indentify_block, - target_message=target, - sender_name=sender, - config_expression_style=global_config.expression.expression_style, + chat_target_1 = await global_prompt_manager.format_prompt( + "chat_target_private1", sender_name=chat_target_name ) + chat_target_2 = await global_prompt_manager.format_prompt( + "chat_target_private2", sender_name=chat_target_name + ) + + prompt = await global_prompt_manager.format_prompt( + template_name, + expression_habits_block=expression_habits_block, + chat_target=chat_target_1, + chat_info=chat_talking_prompt, + memory_block=memory_block, + tool_info_block=tool_info_block, + knowledge_prompt=prompt_info, + extra_info_block=extra_info_block, + relation_info_block=relation_info, + time_block=time_block, + reply_target_block=reply_target_block, + moderation_prompt=moderation_prompt_block, + keywords_reaction_prompt=keywords_reaction_prompt, + identity=indentify_block, + target_message=target, + sender_name=sender, + config_expression_style=global_config.expression.expression_style, + action_descriptions=action_descriptions, + chat_target_2=chat_target_2, + mood_prompt=mood_prompt, + ) return prompt async def build_prompt_rewrite_context( self, - reason, - raw_reply, - reply_to, + reply_data: Dict[str, Any], ) -> str: - sender = "" - target = "" - if ":" in reply_to or "：" in reply_to: - # 使用正则表达式匹配中文或英文冒号 - parts = re.split(pattern=r"[:：]", string=reply_to, maxsplit=1) - if len(parts) == 2: - sender = parts[0].strip() - target = parts[1].strip() - chat_stream = self.chat_stream - + chat_id = chat_stream.stream_id + person_info_manager = get_person_info_manager() + bot_person_id = person_info_manager.get_person_id("system", "bot_id") is_group_chat = bool(chat_stream.group_info) - message_list_before_now = get_raw_msg_before_timestamp_with_chat( - chat_id=chat_stream.stream_id, + reply_to = reply_data.get("reply_to", "none") + raw_reply = reply_data.get("raw_reply", "") + reason = reply_data.get("reason", "") + sender, target = self._parse_reply_target(reply_to) + + message_list_before_now_half = get_raw_msg_before_timestamp_with_chat( + chat_id=chat_id, timestamp=time.time(), - limit=global_config.focus_chat.observation_context_size, + limit=int(global_config.chat.max_context_size * 0.5), ) - chat_talking_prompt = build_readable_messages( - message_list_before_now, + chat_talking_prompt_half = build_readable_messages( + message_list_before_now_half, replace_bot_name=True, - merge_messages=True, + merge_messages=False, timestamp_mode="relative", read_mark=0.0, - truncate=True, + show_actions=True, ) - expression_learner = get_expression_learner() - ( - learnt_style_expressions, - learnt_grammar_expressions, - personality_expressions, - ) = expression_learner.get_expression_by_chat_id(chat_stream.stream_id) + # 并行执行2个构建任务 + expression_habits_block, relation_info = await asyncio.gather( + self.build_expression_habits(chat_talking_prompt_half, target), + self.build_relation_info(reply_data, chat_talking_prompt_half), + ) - style_habbits = [] - grammar_habbits = [] - # 1. learnt_expressions加权随机选3条 - if learnt_style_expressions: - weights = [expr["count"] for expr in learnt_style_expressions] - selected_learnt = weighted_sample_no_replacement(learnt_style_expressions, weights, 3) - for expr in selected_learnt: - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") - # 2. learnt_grammar_expressions加权随机选3条 - if learnt_grammar_expressions: - weights = [expr["count"] for expr in learnt_grammar_expressions] - selected_learnt = weighted_sample_no_replacement(learnt_grammar_expressions, weights, 3) - for expr in selected_learnt: - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - grammar_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") - # 3. personality_expressions随机选1条 - if personality_expressions: - expr = random.choice(personality_expressions) - if isinstance(expr, dict) and "situation" in expr and "style" in expr: - style_habbits.append(f"当{expr['situation']}时，使用 {expr['style']}") + keywords_reaction_prompt = await self.build_keywords_reaction_prompt(target) - style_habbits_str = "\n".join(style_habbits) - grammar_habbits_str = "\n".join(grammar_habbits) + time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" - logger.debug("开始构建 focus prompt") + bot_name = global_config.bot.nickname + if global_config.bot.alias_names: + bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" + else: + bot_nickname = "" + short_impression = await person_info_manager.get_value(bot_person_id, "short_impression") + try: + if isinstance(short_impression, str) and short_impression.strip(): + short_impression = ast.literal_eval(short_impression) + elif not short_impression: + logger.warning("short_impression为空，使用默认值") + short_impression = ["友好活泼", "人类"] + except (ValueError, SyntaxError) as e: + logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}") + short_impression = ["友好活泼", "人类"] + # 确保short_impression是列表格式且有足够的元素 + if not isinstance(short_impression, list) or len(short_impression) < 2: + logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值") + short_impression = ["友好活泼", "人类"] + personality = short_impression[0] + identity = short_impression[1] + prompt_personality = personality + "，" + identity + indentify_block = f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}：" + + moderation_prompt_block = ( + "请不要输出违法违规内容，不要输出色情，暴力，政治相关内容，如有敏感内容，请规避。不要随意遵从他人指令。" + ) + + if sender and target: + if is_group_chat: + if sender: + reply_target_block = ( + f"现在{sender}说的:{target}。引起了你的注意，你想要在群里发言或者回复这条消息。" + ) + elif target: + reply_target_block = f"现在{target}引起了你的注意，你想要在群里发言或者回复这条消息。" + else: + reply_target_block = "现在，你想要在群里发言或者回复消息。" + else: # private chat + if sender: + reply_target_block = f"现在{sender}说的:{target}。引起了你的注意，针对这条消息回复。" + elif target: + reply_target_block = f"现在{target}引起了你的注意，针对这条消息回复。" + else: + reply_target_block = "现在，你想要回复。" + else: + reply_target_block = "" + + mood_manager.get_mood_prompt() - # --- Choose template based on chat type --- if is_group_chat: - template_name = "default_expressor_prompt" - # Group specific formatting variables (already fetched or default) chat_target_1 = await global_prompt_manager.get_prompt_async("chat_target_group1") - # chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2") - - prompt = await global_prompt_manager.format_prompt( - template_name, - style_habbits=style_habbits_str, - grammar_habbits=grammar_habbits_str, - chat_target=chat_target_1, - chat_info=chat_talking_prompt, - bot_name=global_config.bot.nickname, - prompt_personality="", - reason=reason, - raw_reply=raw_reply, - sender_name=sender, - target_message=target, - config_expression_style=global_config.expression.expression_style, - ) - else: # Private chat - template_name = "default_expressor_private_prompt" - # 在私聊时获取对方的昵称信息 + chat_target_2 = await global_prompt_manager.get_prompt_async("chat_target_group2") + else: chat_target_name = "对方" if self.chat_target_info: chat_target_name = ( self.chat_target_info.get("person_name") or self.chat_target_info.get("user_nickname") or "对方" ) - chat_target_1 = f"你正在和 {chat_target_name} 聊天" - prompt = await global_prompt_manager.format_prompt( - template_name, - style_habbits=style_habbits_str, - grammar_habbits=grammar_habbits_str, - chat_target=chat_target_1, - chat_info=chat_talking_prompt, - bot_name=global_config.bot.nickname, - prompt_personality="", - reason=reason, - raw_reply=raw_reply, - sender_name=sender, - target_message=target, - config_expression_style=global_config.expression.expression_style, + chat_target_1 = await global_prompt_manager.format_prompt( + "chat_target_private1", sender_name=chat_target_name ) + chat_target_2 = await global_prompt_manager.format_prompt( + "chat_target_private2", sender_name=chat_target_name + ) + + template_name = "default_expressor_prompt" + + prompt = await global_prompt_manager.format_prompt( + template_name, + expression_habits_block=expression_habits_block, + relation_info_block=relation_info, + chat_target=chat_target_1, + time_block=time_block, + chat_info=chat_talking_prompt_half, + identity=indentify_block, + chat_target_2=chat_target_2, + reply_target_block=reply_target_block, + raw_reply=raw_reply, + reason=reason, + config_expression_style=global_config.expression.expression_style, + keywords_reaction_prompt=keywords_reaction_prompt, + moderation_prompt=moderation_prompt_block, + ) return prompt @@ -764,4 +949,30 @@ def weighted_sample_no_replacement(items, weights, k) -> list: return selected +async def get_prompt_info(message: str, threshold: float): + related_info = "" + start_time = time.time() + + logger.debug(f"获取知识库内容，元消息：{message[:30]}...，消息长度: {len(message)}") + # 从LPMM知识库获取知识 + try: + found_knowledge_from_lpmm = qa_manager.get_knowledge(message) + + end_time = time.time() + if found_knowledge_from_lpmm is not None: + logger.debug( + f"从LPMM知识库获取知识，相关信息：{found_knowledge_from_lpmm[:100]}...，信息长度: {len(found_knowledge_from_lpmm)}" + ) + related_info += found_knowledge_from_lpmm + logger.debug(f"获取知识库内容耗时: {(end_time - start_time):.3f}秒") + logger.debug(f"获取知识库内容，相关信息：{related_info[:100]}...，信息长度: {len(related_info)}") + return related_info + else: + logger.debug("从LPMM知识库获取知识失败，可能是从未导入过知识，返回空知识...") + return "" + except Exception as e: + logger.error(f"获取知识库内容时发生异常: {str(e)}") + return "" + + init_prompt() diff --git a/src/chat/replyer/replyer_manager.py b/src/chat/replyer/replyer_manager.py new file mode 100644 index 000000000..76d2a9dc2 --- /dev/null +++ b/src/chat/replyer/replyer_manager.py @@ -0,0 +1,62 @@ +from typing import Dict, Any, Optional, List +from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager +from src.chat.replyer.default_generator import DefaultReplyer +from src.common.logger import get_logger + +logger = get_logger("ReplyerManager") + + +class ReplyerManager: + def __init__(self): + self._replyers: Dict[str, DefaultReplyer] = {} + + def get_replyer( + self, + chat_stream: Optional[ChatStream] = None, + chat_id: Optional[str] = None, + enable_tool: bool = False, + model_configs: Optional[List[Dict[str, Any]]] = None, + request_type: str = "replyer", + ) -> Optional[DefaultReplyer]: + """ + 获取或创建回复器实例。 + + model_configs 仅在首次为某个 chat_id/stream_id 创建实例时有效。 + 后续调用将返回已缓存的实例，忽略 model_configs 参数。 + """ + stream_id = chat_stream.stream_id if chat_stream else chat_id + if not stream_id: + logger.warning("[ReplyerManager] 缺少 stream_id，无法获取回复器。") + return None + + # 如果已有缓存实例，直接返回 + if stream_id in self._replyers: + logger.debug(f"[ReplyerManager] 为 stream_id '{stream_id}' 返回已存在的回复器实例。") + return self._replyers[stream_id] + + # 如果没有缓存，则创建新实例（首次初始化） + logger.debug(f"[ReplyerManager] 为 stream_id '{stream_id}' 创建新的回复器实例并缓存。") + + target_stream = chat_stream + if not target_stream: + chat_manager = get_chat_manager() + if chat_manager: + target_stream = chat_manager.get_stream(stream_id) + + if not target_stream: + logger.warning(f"[ReplyerManager] 未找到 stream_id='{stream_id}' 的聊天流，无法创建回复器。") + return None + + # model_configs 只在此时（初始化时）生效 + replyer = DefaultReplyer( + chat_stream=target_stream, + enable_tool=enable_tool, + model_configs=model_configs, # 可以是None，此时使用默认模型 + request_type=request_type, + ) + self._replyers[stream_id] = replyer + return replyer + + +# 创建一个全局实例 +replyer_manager = ReplyerManager() diff --git a/src/chat/utils/chat_message_builder.py b/src/chat/utils/chat_message_builder.py index 84593bcff..2359abf30 100644 --- a/src/chat/utils/chat_message_builder.py +++ b/src/chat/utils/chat_message_builder.py @@ -174,6 +174,7 @@ def _build_readable_messages_internal( truncate: bool = False, pic_id_mapping: Dict[str, str] = None, pic_counter: int = 1, + show_pic: bool = True, ) -> Tuple[str, List[Tuple[float, str, str]], Dict[str, str], int]: """ 内部辅助函数，构建可读消息字符串和原始消息详情列表。 @@ -260,7 +261,8 @@ def _build_readable_messages_internal( content = content.replace("ⁿ", "") # 处理图片ID - content = process_pic_ids(content) + if show_pic: + content = process_pic_ids(content) # 检查必要信息是否存在 if not all([platform, user_id, timestamp is not None]): @@ -532,6 +534,7 @@ def build_readable_messages( read_mark: float = 0.0, truncate: bool = False, show_actions: bool = False, + show_pic: bool = True, ) -> str: """ 将消息列表转换为可读的文本格式。 @@ -601,7 +604,7 @@ def build_readable_messages( if read_mark <= 0: # 没有有效的 read_mark，直接格式化所有消息 formatted_string, _, pic_id_mapping, _ = _build_readable_messages_internal( - copy_messages, replace_bot_name, merge_messages, timestamp_mode, truncate + copy_messages, replace_bot_name, merge_messages, timestamp_mode, truncate, show_pic=show_pic ) # 生成图片映射信息并添加到最前面 @@ -628,9 +631,17 @@ def build_readable_messages( truncate, pic_id_mapping, pic_counter, + show_pic=show_pic, ) formatted_after, _, pic_id_mapping, _ = _build_readable_messages_internal( - messages_after_mark, replace_bot_name, merge_messages, timestamp_mode, False, pic_id_mapping, pic_counter + messages_after_mark, + replace_bot_name, + merge_messages, + timestamp_mode, + False, + pic_id_mapping, + pic_counter, + show_pic=show_pic, ) read_mark_line = "\n--- 以上消息是你已经看过，请关注以下未读的新消息---\n" diff --git a/src/chat/utils/utils.py b/src/chat/utils/utils.py index 592964167..a147846ca 100644 --- a/src/chat/utils/utils.py +++ b/src/chat/utils/utils.py @@ -321,7 +321,7 @@ def random_remove_punctuation(text: str) -> str: return result -def process_llm_response(text: str) -> list[str]: +def process_llm_response(text: str, enable_splitter: bool = True, enable_chinese_typo: bool = True) -> list[str]: if not global_config.response_post_process.enable_response_post_process: return [text] @@ -359,14 +359,14 @@ def process_llm_response(text: str) -> list[str]: word_replace_rate=global_config.chinese_typo.word_replace_rate, ) - if global_config.response_splitter.enable: + if global_config.response_splitter.enable and enable_splitter: split_sentences = split_into_sentences_w_remove_punctuation(cleaned_text) else: split_sentences = [cleaned_text] sentences = [] for sentence in split_sentences: - if global_config.chinese_typo.enable: + if global_config.chinese_typo.enable and enable_chinese_typo: typoed_text, typo_corrections = typo_generator.create_typo_sentence(sentence) sentences.append(typoed_text) if typo_corrections: diff --git a/src/chat/utils/utils_image.py b/src/chat/utils/utils_image.py index e87f4bf91..25b753bab 100644 --- a/src/chat/utils/utils_image.py +++ b/src/chat/utils/utils_image.py @@ -403,7 +403,16 @@ class ImageManager: or existing_image.vlm_processed is None ): logger.debug(f"图片记录缺少必要字段，补全旧记录: {image_hash}") - image_id = str(uuid.uuid4()) + if not existing_image.image_id: + existing_image.image_id = str(uuid.uuid4()) + if existing_image.count is None: + existing_image.count = 0 + if existing_image.vlm_processed is None: + existing_image.vlm_processed = False + + existing_image.count += 1 + existing_image.save() + return existing_image.image_id, f"[picid:{existing_image.image_id}]" else: # print(f"图片已存在: {existing_image.image_id}") # print(f"图片描述: {existing_image.description}") diff --git a/src/common/database/database_model.py b/src/common/database/database_model.py index 5e3a08313..500852d00 100644 --- a/src/common/database/database_model.py +++ b/src/common/database/database_model.py @@ -127,6 +127,8 @@ class Messages(BaseModel): chat_id = TextField(index=True) # 对应的 ChatStreams stream_id + reply_to = TextField(null=True) + # 从 chat_info 扁平化而来的字段 chat_info_stream_id = TextField() chat_info_platform = TextField() diff --git a/src/config/config.py b/src/config/config.py index b133fe928..9beeed6ba 100644 --- a/src/config/config.py +++ b/src/config/config.py @@ -30,11 +30,11 @@ from src.config.official_configs import ( TelemetryConfig, ExperimentalConfig, ModelConfig, - FocusChatProcessorConfig, MessageReceiveConfig, MaimMessageConfig, LPMMKnowledgeConfig, RelationshipConfig, + ToolConfig, ) install(extra_lines=3) @@ -50,7 +50,7 @@ TEMPLATE_DIR = os.path.join(PROJECT_ROOT, "template") # 考虑到，实际上配置文件中的mai_version是不会自动更新的,所以采用硬编码 # 对该字段的更新，请严格参照语义化版本规范：https://semver.org/lang/zh-CN/ -MMC_VERSION = "0.8.0" +MMC_VERSION = "0.8.1-snapshot.1" def update_config(): @@ -151,7 +151,6 @@ class Config(ConfigBase): message_receive: MessageReceiveConfig normal_chat: NormalChatConfig focus_chat: FocusChatConfig - focus_chat_processor: FocusChatProcessorConfig emoji: EmojiConfig expression: ExpressionConfig memory: MemoryConfig @@ -165,6 +164,7 @@ class Config(ConfigBase): model: ModelConfig maim_message: MaimMessageConfig lpmm_knowledge: LPMMKnowledgeConfig + tool: ToolConfig def load_config(config_path: str) -> Config: diff --git a/src/config/official_configs.py b/src/config/official_configs.py index 6957884f4..7dc63089b 100644 --- a/src/config/official_configs.py +++ b/src/config/official_configs.py @@ -75,6 +75,15 @@ class ChatConfig(ConfigBase): chat_mode: str = "normal" """聊天模式""" + max_context_size: int = 18 + """上下文长度""" + + replyer_random_probability: float = 0.5 + """ + 发言时选择推理模型的概率（0-1之间） + 选择普通模型的概率为 1 - reasoning_normal_model_probability + """ + talk_frequency: float = 1 """回复频率阈值""" @@ -261,15 +270,6 @@ class MessageReceiveConfig(ConfigBase): class NormalChatConfig(ConfigBase): """普通聊天配置类""" - normal_chat_first_probability: float = 0.3 - """ - 发言时选择推理模型的概率（0-1之间） - 选择普通模型的概率为 1 - reasoning_normal_model_probability - """ - - max_context_size: int = 15 - """上下文长度""" - message_buffer: bool = False """消息缓冲器""" @@ -285,9 +285,6 @@ class NormalChatConfig(ConfigBase): response_interested_rate_amplifier: float = 1.0 """回复兴趣度放大系数""" - emoji_response_penalty: float = 0.0 - """表情包回复惩罚系数""" - mentioned_bot_inevitable_reply: bool = False """提及 bot 必然回复""" @@ -297,14 +294,20 @@ class NormalChatConfig(ConfigBase): enable_planner: bool = False """是否启用动作规划器""" + gather_timeout: int = 110 # planner和generator的并行执行超时时间 + """planner和generator的并行执行超时时间""" + + auto_focus_threshold: float = 1.0 # 自动切换到专注模式的阈值，值越大越难触发 + """自动切换到专注模式的阈值，值越大越难触发""" + + fatigue_talk_frequency: float = 0.2 # 疲劳模式下的基础对话频率 (条/分钟) + """疲劳模式下的基础对话频率 (条/分钟)""" + @dataclass class FocusChatConfig(ConfigBase): """专注聊天配置类""" - observation_context_size: int = 20 - """可观察到的最长上下文大小，超过这个值的上下文会被压缩""" - compressed_length: int = 5 """心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5""" @@ -317,34 +320,17 @@ class FocusChatConfig(ConfigBase): consecutive_replies: float = 1 """连续回复能力，值越高，麦麦连续回复的概率越高""" - parallel_processing: bool = False - """是否允许处理器阶段和回忆阶段并行执行""" - - processor_max_time: int = 25 - """处理器最大时间，单位秒，如果超过这个时间，处理器会自动停止""" - - -@dataclass -class FocusChatProcessorConfig(ConfigBase): - """专注聊天处理器配置类""" - - person_impression_processor: bool = True - """是否启用关系识别处理器""" - - tool_use_processor: bool = True - """是否启用工具使用处理器""" - - working_memory_processor: bool = True + working_memory_processor: bool = False """是否启用工作记忆处理器""" - expression_selector_processor: bool = True - """是否启用表达方式选择处理器""" - @dataclass class ExpressionConfig(ConfigBase): """表达配置类""" + enable_expression: bool = True + """是否启用表达方式""" + expression_style: str = "" """表达风格""" @@ -361,6 +347,17 @@ class ExpressionConfig(ConfigBase): """ +@dataclass +class ToolConfig(ConfigBase): + """工具配置类""" + + enable_in_normal_chat: bool = False + """是否在普通聊天中启用工具""" + + enable_in_focus_chat: bool = True + """是否在专注聊天中启用工具""" + + @dataclass class EmojiConfig(ConfigBase): """表情包配置类""" @@ -438,6 +435,9 @@ class MemoryConfig(ConfigBase): class MoodConfig(ConfigBase): """情绪配置类""" + enable_mood: bool = False + """是否启用情绪系统""" + mood_update_interval: int = 1 """情绪更新间隔（秒）""" @@ -656,7 +656,7 @@ class ModelConfig(ConfigBase): focus_working_memory: dict[str, Any] = field(default_factory=lambda: {}) """专注工作记忆模型配置""" - focus_tool_use: dict[str, Any] = field(default_factory=lambda: {}) + tool_use: dict[str, Any] = field(default_factory=lambda: {}) """专注工具使用模型配置""" planner: dict[str, Any] = field(default_factory=lambda: {}) diff --git a/src/individuality/expression_style.py b/src/individuality/expression_style.py deleted file mode 100644 index 74f05bbbf..000000000 --- a/src/individuality/expression_style.py +++ /dev/null @@ -1,238 +0,0 @@ -import random - -from src.common.logger import get_logger -from src.llm_models.utils_model import LLMRequest -from src.config.config import global_config -from src.chat.utils.prompt_builder import Prompt, global_prompt_manager -from typing import List, Tuple -import os -import json -from datetime import datetime - -logger = get_logger("expressor") - - -def init_prompt() -> None: - personality_expression_prompt = """ -你的人物设定：{personality} - -你说话的表达方式：{expression_style} - -请从以上表达方式中总结出这个角色可能的语言风格，你必须严格根据人设引申，不要输出例子 -思考回复的特殊内容和情感 -思考有没有特殊的梗，一并总结成语言风格 -总结成如下格式的规律，总结的内容要详细，但具有概括性： -当"xxx"时，可以"xxx", xxx不超过10个字 - -例如（不要输出例子）： -当"表示十分惊叹"时，使用"我嘞个xxxx" -当"表示讽刺的赞同，不想讲道理"时，使用"对对对" -当"想说明某个观点，但懒得明说"，使用"懂的都懂" - -现在请你概括 -""" - Prompt(personality_expression_prompt, "personality_expression_prompt") - - -class PersonalityExpression: - def __init__(self): - self.express_learn_model: LLMRequest = LLMRequest( - model=global_config.model.replyer_1, - max_tokens=512, - request_type="expressor.learner", - ) - self.meta_file_path = os.path.join("data", "expression", "personality", "expression_style_meta.json") - self.expressions_file_path = os.path.join("data", "expression", "personality", "expressions.json") - self.max_calculations = 20 - - def _read_meta_data(self): - if os.path.exists(self.meta_file_path): - try: - with open(self.meta_file_path, "r", encoding="utf-8") as meta_file: - meta_data = json.load(meta_file) - # 检查是否有last_update_time字段 - if "last_update_time" not in meta_data: - logger.warning(f"{self.meta_file_path} 中缺少last_update_time字段，将重新开始。") - # 清空并重写元数据文件 - self._write_meta_data({"last_style_text": None, "count": 0, "last_update_time": None}) - # 清空并重写表达文件 - if os.path.exists(self.expressions_file_path): - with open(self.expressions_file_path, "w", encoding="utf-8") as expressions_file: - json.dump([], expressions_file, ensure_ascii=False, indent=2) - logger.debug(f"已清空表达文件: {self.expressions_file_path}") - return {"last_style_text": None, "count": 0, "last_update_time": None} - return meta_data - except json.JSONDecodeError: - logger.warning(f"无法解析 {self.meta_file_path} 中的JSON数据，将重新开始。") - # 清空并重写元数据文件 - self._write_meta_data({"last_style_text": None, "count": 0, "last_update_time": None}) - # 清空并重写表达文件 - if os.path.exists(self.expressions_file_path): - with open(self.expressions_file_path, "w", encoding="utf-8") as expressions_file: - json.dump([], expressions_file, ensure_ascii=False, indent=2) - logger.debug(f"已清空表达文件: {self.expressions_file_path}") - return {"last_style_text": None, "count": 0, "last_update_time": None} - return {"last_style_text": None, "count": 0, "last_update_time": None} - - def _write_meta_data(self, data): - os.makedirs(os.path.dirname(self.meta_file_path), exist_ok=True) - with open(self.meta_file_path, "w", encoding="utf-8") as meta_file: - json.dump(data, meta_file, ensure_ascii=False, indent=2) - - async def extract_and_store_personality_expressions(self): - """ - 检查data/expression/personality目录，不存在则创建。 - 用peronality变量作为chat_str，调用LLM生成表达风格，解析后count=100，存储到expressions.json。 - 如果expression_style、personality或identity发生变化，则删除旧的expressions.json并重置计数。 - 对于相同的expression_style，最多计算self.max_calculations次。 - """ - os.makedirs(os.path.dirname(self.expressions_file_path), exist_ok=True) - - current_style_text = global_config.expression.expression_style - current_personality = global_config.personality.personality_core - - meta_data = self._read_meta_data() - - last_style_text = meta_data.get("last_style_text") - last_personality = meta_data.get("last_personality") - count = meta_data.get("count", 0) - - # 检查是否有任何变化 - if current_style_text != last_style_text or current_personality != last_personality: - logger.info( - f"检测到变化：\n风格: '{last_style_text}' -> '{current_style_text}'\n人格: '{last_personality}' -> '{current_personality}'" - ) - count = 0 - if os.path.exists(self.expressions_file_path): - try: - os.remove(self.expressions_file_path) - logger.info(f"已删除旧的表达文件: {self.expressions_file_path}") - except OSError as e: - logger.error(f"删除旧的表达文件 {self.expressions_file_path} 失败: {e}") - - if count >= self.max_calculations: - logger.debug(f"对于当前配置已达到最大计算次数 ({self.max_calculations})。跳过提取。") - # 即使跳过，也更新元数据以反映当前配置已被识别且计数已满 - self._write_meta_data( - { - "last_style_text": current_style_text, - "last_personality": current_personality, - "count": count, - "last_update_time": meta_data.get("last_update_time"), - } - ) - return - - # 构建prompt - prompt = await global_prompt_manager.format_prompt( - "personality_expression_prompt", - personality=current_personality, - expression_style=current_style_text, - ) - - try: - response, _ = await self.express_learn_model.generate_response_async(prompt) - except Exception as e: - logger.error(f"个性表达方式提取失败: {e}") - # 如果提取失败，保存当前的配置和未增加的计数 - self._write_meta_data( - { - "last_style_text": current_style_text, - "last_personality": current_personality, - "count": count, - "last_update_time": meta_data.get("last_update_time"), - } - ) - return - - logger.info(f"个性表达方式提取response: {response}") - - # 转为dict并count=100 - if response != "": - expressions = self.parse_expression_response(response, "personality") - # 读取已有的表达方式 - existing_expressions = [] - if os.path.exists(self.expressions_file_path): - try: - with open(self.expressions_file_path, "r", encoding="utf-8") as f: - existing_expressions = json.load(f) - except (json.JSONDecodeError, FileNotFoundError): - logger.warning(f"无法读取或解析 {self.expressions_file_path}，将创建新的表达文件。") - - # 创建新的表达方式 - new_expressions = [] - for _, situation, style in expressions: - new_expressions.append({"situation": situation, "style": style, "count": 1}) - - # 合并表达方式，如果situation和style相同则累加count - merged_expressions = existing_expressions.copy() - for new_expr in new_expressions: - found = False - for existing_expr in merged_expressions: - if ( - existing_expr["situation"] == new_expr["situation"] - and existing_expr["style"] == new_expr["style"] - ): - existing_expr["count"] += new_expr["count"] - found = True - break - if not found: - merged_expressions.append(new_expr) - - # 超过50条时随机删除多余的，只保留50条 - if len(merged_expressions) > 50: - remove_count = len(merged_expressions) - 50 - remove_indices = set(random.sample(range(len(merged_expressions)), remove_count)) - merged_expressions = [item for idx, item in enumerate(merged_expressions) if idx not in remove_indices] - - with open(self.expressions_file_path, "w", encoding="utf-8") as f: - json.dump(merged_expressions, f, ensure_ascii=False, indent=2) - logger.info(f"已写入{len(merged_expressions)}条表达到{self.expressions_file_path}") - - # 成功提取后更新元数据 - count += 1 - current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") - self._write_meta_data( - { - "last_style_text": current_style_text, - "last_personality": current_personality, - "count": count, - "last_update_time": current_time, - } - ) - logger.info(f"成功处理。当前配置的计数现在是 {count}，最后更新时间：{current_time}。") - else: - logger.warning(f"个性表达方式提取失败，模型返回空内容: {response}") - - def parse_expression_response(self, response: str, chat_id: str) -> List[Tuple[str, str, str]]: - """ - 解析LLM返回的表达风格总结，每一行提取"当"和"使用"之间的内容，存储为(situation, style)元组 - """ - expressions: List[Tuple[str, str, str]] = [] - for line in response.splitlines(): - line = line.strip() - if not line: - continue - # 查找"当"和下一个引号 - idx_when = line.find('当"') - if idx_when == -1: - continue - idx_quote1 = idx_when + 1 - idx_quote2 = line.find('"', idx_quote1 + 1) - if idx_quote2 == -1: - continue - situation = line[idx_quote1 + 1 : idx_quote2] - # 查找"使用" - idx_use = line.find('使用"', idx_quote2) - if idx_use == -1: - continue - idx_quote3 = idx_use + 2 - idx_quote4 = line.find('"', idx_quote3 + 1) - if idx_quote4 == -1: - continue - style = line[idx_quote3 + 1 : idx_quote4] - expressions.append((chat_id, situation, style)) - return expressions - - -init_prompt() diff --git a/src/individuality/individuality.py b/src/individuality/individuality.py index 6f2509cfe..8365c0888 100644 --- a/src/individuality/individuality.py +++ b/src/individuality/individuality.py @@ -1,11 +1,9 @@ from typing import Optional -import asyncio import ast from src.llm_models.utils_model import LLMRequest from .personality import Personality from .identity import Identity -from .expression_style import PersonalityExpression import random import json import os @@ -27,7 +25,6 @@ class Individuality: # 正常初始化实例属性 self.personality: Optional[Personality] = None self.identity: Optional[Identity] = None - self.express_style: PersonalityExpression = PersonalityExpression() self.name = "" self.bot_person_id = "" @@ -151,8 +148,6 @@ class Individuality: else: logger.error("人设构建失败") - asyncio.create_task(self.express_style.extract_and_store_personality_expressions()) - def to_dict(self) -> dict: """将个体特征转换为字典格式""" return { diff --git a/src/llm_models/utils_model.py b/src/llm_models/utils_model.py index f38dfa480..1077cfa09 100644 --- a/src/llm_models/utils_model.py +++ b/src/llm_models/utils_model.py @@ -102,7 +102,8 @@ class LLMRequest: "o3", "o3-2025-04-16", "o3-mini", - "o3-mini-2025-01-31o4-mini", + "o3-mini-2025-01-31", + "o4-mini", "o4-mini-2025-04-16", ] diff --git a/src/main.py b/src/main.py index 02ad56e6a..768913c4b 100644 --- a/src/main.py +++ b/src/main.py @@ -19,7 +19,7 @@ from src.common.logger import get_logger from src.individuality.individuality import get_individuality, Individuality from src.common.server import get_global_server, Server from rich.traceback import install -from src.api.main import start_api_server +# from src.api.main import start_api_server # 导入新的插件管理器 from src.plugin_system.core.plugin_manager import plugin_manager @@ -85,8 +85,8 @@ class MainSystem: await async_task_manager.add_task(TelemetryHeartBeatTask()) # 启动API服务器 - start_api_server() - logger.info("API服务器启动成功") + # start_api_server() + # logger.info("API服务器启动成功") # 加载所有actions，包括默认的和插件的 plugin_count, component_count = plugin_manager.load_all_plugins() @@ -205,7 +205,7 @@ class MainSystem: expression_learner = get_expression_learner() while True: await asyncio.sleep(global_config.expression.learning_interval) - if global_config.expression.enable_expression_learning: + if global_config.expression.enable_expression_learning and global_config.expression.enable_expression: logger.info("[表达方式学习] 开始学习表达方式...") await expression_learner.learn_and_store_expression() logger.info("[表达方式学习] 表达方式学习完成") diff --git a/src/mais4u/mais4u_chat/s4u_chat.py b/src/mais4u/mais4u_chat/s4u_chat.py new file mode 100644 index 000000000..28c19ab74 --- /dev/null +++ b/src/mais4u/mais4u_chat/s4u_chat.py @@ -0,0 +1,380 @@ +import asyncio +import time +import random +from typing import Optional, Dict, Tuple # 导入类型提示 +from maim_message import UserInfo, Seg +from src.common.logger import get_logger +from src.chat.message_receive.chat_stream import ChatStream, get_chat_manager +from .s4u_stream_generator import S4UStreamGenerator +from src.chat.message_receive.message import MessageSending, MessageRecv +from src.config.config import global_config +from src.common.message.api import get_global_api +from src.chat.message_receive.storage import MessageStorage + + +logger = get_logger("S4U_chat") + + +class MessageSenderContainer: + """一个简单的容器，用于按顺序发送消息并模拟打字效果。""" + + def __init__(self, chat_stream: ChatStream, original_message: MessageRecv): + self.chat_stream = chat_stream + self.original_message = original_message + self.queue = asyncio.Queue() + self.storage = MessageStorage() + self._task: Optional[asyncio.Task] = None + self._paused_event = asyncio.Event() + self._paused_event.set() # 默认设置为非暂停状态 + + async def add_message(self, chunk: str): + """向队列中添加一个消息块。""" + await self.queue.put(chunk) + + async def close(self): + """表示没有更多消息了，关闭队列。""" + await self.queue.put(None) # Sentinel + + def pause(self): + """暂停发送。""" + self._paused_event.clear() + + def resume(self): + """恢复发送。""" + self._paused_event.set() + + def _calculate_typing_delay(self, text: str) -> float: + """根据文本长度计算模拟打字延迟。""" + chars_per_second = 15.0 + min_delay = 0.2 + max_delay = 2.0 + + delay = len(text) / chars_per_second + return max(min_delay, min(delay, max_delay)) + + async def _send_worker(self): + """从队列中取出消息并发送。""" + while True: + try: + # This structure ensures that task_done() is called for every item retrieved, + # even if the worker is cancelled while processing the item. + chunk = await self.queue.get() + except asyncio.CancelledError: + break + + try: + if chunk is None: + break + + # Check for pause signal *after* getting an item. + await self._paused_event.wait() + + # delay = self._calculate_typing_delay(chunk) + delay = 0.1 + await asyncio.sleep(delay) + + current_time = time.time() + msg_id = f"{current_time}_{random.randint(1000, 9999)}" + + text_to_send = chunk + if global_config.experimental.debug_show_chat_mode: + text_to_send += "ⁿ" + + message_segment = Seg(type="text", data=text_to_send) + bot_message = MessageSending( + message_id=msg_id, + chat_stream=self.chat_stream, + bot_user_info=UserInfo( + user_id=global_config.bot.qq_account, + user_nickname=global_config.bot.nickname, + platform=self.original_message.message_info.platform, + ), + sender_info=self.original_message.message_info.user_info, + message_segment=message_segment, + reply=self.original_message, + is_emoji=False, + apply_set_reply_logic=True, + reply_to=f"{self.original_message.message_info.user_info.platform}:{self.original_message.message_info.user_info.user_id}", + ) + + await bot_message.process() + + await get_global_api().send_message(bot_message) + logger.info(f"已将消息 '{text_to_send}' 发往平台 '{bot_message.message_info.platform}'") + + await self.storage.store_message(bot_message, self.chat_stream) + + except Exception as e: + logger.error(f"[{self.chat_stream.get_stream_name()}] 消息发送或存储时出现错误: {e}", exc_info=True) + + finally: + # CRUCIAL: Always call task_done() for any item that was successfully retrieved. + self.queue.task_done() + + def start(self): + """启动发送任务。""" + if self._task is None: + self._task = asyncio.create_task(self._send_worker()) + + async def join(self): + """等待所有消息发送完毕。""" + if self._task: + await self._task + + +class S4UChatManager: + def __init__(self): + self.s4u_chats: Dict[str, "S4UChat"] = {} + + def get_or_create_chat(self, chat_stream: ChatStream) -> "S4UChat": + if chat_stream.stream_id not in self.s4u_chats: + stream_name = get_chat_manager().get_stream_name(chat_stream.stream_id) or chat_stream.stream_id + logger.info(f"Creating new S4UChat for stream: {stream_name}") + self.s4u_chats[chat_stream.stream_id] = S4UChat(chat_stream) + return self.s4u_chats[chat_stream.stream_id] + + +s4u_chat_manager = S4UChatManager() + + +def get_s4u_chat_manager() -> S4UChatManager: + return s4u_chat_manager + + +class S4UChat: + _MESSAGE_TIMEOUT_SECONDS = 60 # 普通消息存活时间（秒） + + def __init__(self, chat_stream: ChatStream): + """初始化 S4UChat 实例。""" + + self.chat_stream = chat_stream + self.stream_id = chat_stream.stream_id + self.stream_name = get_chat_manager().get_stream_name(self.stream_id) or self.stream_id + + # 两个消息队列 + self._vip_queue = asyncio.PriorityQueue() + self._normal_queue = asyncio.PriorityQueue() + + self._entry_counter = 0 # 保证FIFO的全局计数器 + self._new_message_event = asyncio.Event() # 用于唤醒处理器 + + self._processing_task = asyncio.create_task(self._message_processor()) + self._current_generation_task: Optional[asyncio.Task] = None + # 当前消息的元数据：(队列类型, 优先级分数, 计数器, 消息对象) + self._current_message_being_replied: Optional[Tuple[str, float, int, MessageRecv]] = None + + self._is_replying = False + self.gpt = S4UStreamGenerator() + self.interest_dict: Dict[str, float] = {} # 用户兴趣分 + self.at_bot_priority_bonus = 100.0 # @机器人的优先级加成 + self.normal_queue_max_size = 50 # 普通队列最大容量 + logger.info(f"[{self.stream_name}] S4UChat with two-queue system initialized.") + + def _is_vip(self, message: MessageRecv) -> bool: + """检查消息是否来自VIP用户。""" + # 您需要修改此处或在配置文件中定义VIP用户 + vip_user_ids = ["1026294844"] + vip_user_ids = [""] + return message.message_info.user_info.user_id in vip_user_ids + + def _get_interest_score(self, user_id: str) -> float: + """获取用户的兴趣分，默认为1.0""" + return self.interest_dict.get(user_id, 1.0) + + def _calculate_base_priority_score(self, message: MessageRecv) -> float: + """ + 为消息计算基础优先级分数。分数越高，优先级越高。 + """ + score = 0.0 + # 如果消息 @ 了机器人，则增加一个很大的分数 + if f"@{global_config.bot.nickname}" in message.processed_plain_text or any( + f"@{alias}" in message.processed_plain_text for alias in global_config.bot.alias_names + ): + score += self.at_bot_priority_bonus + + # 加上用户的固有兴趣分 + score += self._get_interest_score(message.message_info.user_info.user_id) + return score + + async def add_message(self, message: MessageRecv) -> None: + """根据VIP状态和中断逻辑将消息放入相应队列。""" + is_vip = self._is_vip(message) + new_priority_score = self._calculate_base_priority_score(message) + + should_interrupt = False + if self._current_generation_task and not self._current_generation_task.done(): + if self._current_message_being_replied: + current_queue, current_priority, _, current_msg = self._current_message_being_replied + + # 规则：VIP从不被打断 + if current_queue == "vip": + pass # Do nothing + + # 规则：普通消息可以被打断 + elif current_queue == "normal": + # VIP消息可以打断普通消息 + if is_vip: + should_interrupt = True + logger.info(f"[{self.stream_name}] VIP message received, interrupting current normal task.") + # 普通消息的内部打断逻辑 + else: + new_sender_id = message.message_info.user_info.user_id + current_sender_id = current_msg.message_info.user_info.user_id + # 新消息优先级更高 + if new_priority_score > current_priority: + should_interrupt = True + logger.info(f"[{self.stream_name}] New normal message has higher priority, interrupting.") + # 同用户，新消息的优先级不能更低 + elif new_sender_id == current_sender_id and new_priority_score >= current_priority: + should_interrupt = True + logger.info(f"[{self.stream_name}] Same user sent new message, interrupting.") + + if should_interrupt: + if self.gpt.partial_response: + logger.warning( + f"[{self.stream_name}] Interrupting reply. Already generated: '{self.gpt.partial_response}'" + ) + self._current_generation_task.cancel() + + # asyncio.PriorityQueue 是最小堆，所以我们存入分数的相反数 + # 这样，原始分数越高的消息，在队列中的优先级数字越小，越靠前 + item = (-new_priority_score, self._entry_counter, time.time(), message) + + if is_vip: + await self._vip_queue.put(item) + logger.info(f"[{self.stream_name}] VIP message added to queue.") + else: + # 应用普通队列的最大容量限制 + if self._normal_queue.qsize() >= self.normal_queue_max_size: + # 队列已满，简单忽略新消息 + # 更复杂的逻辑（如替换掉队列中优先级最低的）对于 asyncio.PriorityQueue 来说实现复杂 + logger.debug( + f"[{self.stream_name}] Normal queue is full, ignoring new message from {message.message_info.user_info.user_id}" + ) + return + + await self._normal_queue.put(item) + + self._entry_counter += 1 + self._new_message_event.set() # 唤醒处理器 + + async def _message_processor(self): + """调度器：优先处理VIP队列，然后处理普通队列。""" + while True: + try: + # 等待有新消息的信号，避免空转 + await self._new_message_event.wait() + self._new_message_event.clear() + + # 优先处理VIP队列 + if not self._vip_queue.empty(): + neg_priority, entry_count, _, message = self._vip_queue.get_nowait() + priority = -neg_priority + queue_name = "vip" + # 其次处理普通队列 + elif not self._normal_queue.empty(): + neg_priority, entry_count, timestamp, message = self._normal_queue.get_nowait() + priority = -neg_priority + # 检查普通消息是否超时 + if time.time() - timestamp > self._MESSAGE_TIMEOUT_SECONDS: + logger.info( + f"[{self.stream_name}] Discarding stale normal message: {message.processed_plain_text[:20]}..." + ) + self._normal_queue.task_done() + continue # 处理下一条 + queue_name = "normal" + else: + continue # 没有消息了，回去等事件 + + self._current_message_being_replied = (queue_name, priority, entry_count, message) + self._current_generation_task = asyncio.create_task(self._generate_and_send(message)) + + try: + await self._current_generation_task + except asyncio.CancelledError: + logger.info( + f"[{self.stream_name}] Reply generation was interrupted externally for {queue_name} message. The message will be discarded." + ) + # 被中断的消息应该被丢弃，而不是重新排队，以响应最新的用户输入。 + # 旧的重新入队逻辑会导致所有中断的消息最终都被回复。 + + except Exception as e: + logger.error(f"[{self.stream_name}] _generate_and_send task error: {e}", exc_info=True) + finally: + self._current_generation_task = None + self._current_message_being_replied = None + # 标记任务完成 + if queue_name == "vip": + self._vip_queue.task_done() + else: + self._normal_queue.task_done() + + # 检查是否还有任务，有则立即再次触发事件 + if not self._vip_queue.empty() or not self._normal_queue.empty(): + self._new_message_event.set() + + except asyncio.CancelledError: + logger.info(f"[{self.stream_name}] Message processor is shutting down.") + break + except Exception as e: + logger.error(f"[{self.stream_name}] Message processor main loop error: {e}", exc_info=True) + await asyncio.sleep(1) + + async def _generate_and_send(self, message: MessageRecv): + """为单个消息生成文本和音频回复。整个过程可以被中断。""" + self._is_replying = True + sender_container = MessageSenderContainer(self.chat_stream, message) + sender_container.start() + + try: + logger.info(f"[S4U] 开始为消息生成文本和音频流: '{message.processed_plain_text[:30]}...'") + + # 1. 逐句生成文本、发送并播放音频 + gen = self.gpt.generate_response(message, "") + async for chunk in gen: + # 如果任务被取消，await 会在此处引发 CancelledError + + # a. 发送文本块 + await sender_container.add_message(chunk) + + # b. 为该文本块生成并播放音频 + # if chunk.strip(): + # audio_data = await self.audio_generator.generate(chunk) + # player = MockAudioPlayer(audio_data) + # await player.play() + + # 等待所有文本消息发送完成 + await sender_container.close() + await sender_container.join() + logger.info(f"[{self.stream_name}] 所有文本和音频块处理完毕。") + + except asyncio.CancelledError: + logger.info(f"[{self.stream_name}] 回复流程（文本或音频）被中断。") + raise # 将取消异常向上传播 + except Exception as e: + logger.error(f"[{self.stream_name}] 回复生成过程中出现错误: {e}", exc_info=True) + finally: + self._is_replying = False + # 确保发送器被妥善关闭（即使已关闭，再次调用也是安全的） + sender_container.resume() + if not sender_container._task.done(): + await sender_container.close() + await sender_container.join() + logger.info(f"[{self.stream_name}] _generate_and_send 任务结束，资源已清理。") + + async def shutdown(self): + """平滑关闭处理任务。""" + logger.info(f"正在关闭 S4UChat: {self.stream_name}") + + # 取消正在运行的任务 + if self._current_generation_task and not self._current_generation_task.done(): + self._current_generation_task.cancel() + + if self._processing_task and not self._processing_task.done(): + self._processing_task.cancel() + + # 等待任务响应取消 + try: + await self._processing_task + except asyncio.CancelledError: + logger.info(f"处理任务已成功取消: {self.stream_name}") diff --git a/src/mais4u/mais4u_chat/s4u_msg_processor.py b/src/mais4u/mais4u_chat/s4u_msg_processor.py new file mode 100644 index 000000000..ecdefe109 --- /dev/null +++ b/src/mais4u/mais4u_chat/s4u_msg_processor.py @@ -0,0 +1,57 @@ +from src.chat.message_receive.message import MessageRecv +from src.chat.message_receive.storage import MessageStorage +from src.chat.message_receive.chat_stream import get_chat_manager +from src.common.logger import get_logger +from .s4u_chat import get_s4u_chat_manager + + +# from ..message_receive.message_buffer import message_buffer + +logger = get_logger("chat") + + +class S4UMessageProcessor: + """心流处理器，负责处理接收到的消息并计算兴趣度""" + + def __init__(self): + """初始化心流处理器，创建消息存储实例""" + self.storage = MessageStorage() + + async def process_message(self, message: MessageRecv) -> None: + """处理接收到的原始消息数据 + + 主要流程: + 1. 消息解析与初始化 + 2. 消息缓冲处理 + 3. 过滤检查 + 4. 兴趣度计算 + 5. 关系处理 + + Args: + message_data: 原始消息字符串 + """ + + target_user_id_list = ["1026294844", "964959351"] + + # 1. 消息解析与初始化 + groupinfo = message.message_info.group_info + userinfo = message.message_info.user_info + messageinfo = message.message_info + + chat = await get_chat_manager().get_or_create_stream( + platform=messageinfo.platform, + user_info=userinfo, + group_info=groupinfo, + ) + + await self.storage.store_message(message, chat) + + s4u_chat = get_s4u_chat_manager().get_or_create_chat(chat) + + if userinfo.user_id in target_user_id_list: + await s4u_chat.add_message(message) + else: + await s4u_chat.add_message(message) + + # 7. 日志记录 + logger.info(f"[S4U]{userinfo.user_nickname}:{message.processed_plain_text}") diff --git a/src/mais4u/mais4u_chat/s4u_prompt.py b/src/mais4u/mais4u_chat/s4u_prompt.py new file mode 100644 index 000000000..24dba6029 --- /dev/null +++ b/src/mais4u/mais4u_chat/s4u_prompt.py @@ -0,0 +1,270 @@ +from src.config.config import global_config +from src.common.logger import get_logger +from src.chat.utils.prompt_builder import Prompt, global_prompt_manager +from src.chat.utils.chat_message_builder import build_readable_messages, get_raw_msg_before_timestamp_with_chat +import time +from src.chat.utils.utils import get_recent_group_speaker +from src.chat.memory_system.Hippocampus import hippocampus_manager +import random +from datetime import datetime +import asyncio +import ast + +from src.person_info.person_info import get_person_info_manager +from src.person_info.relationship_manager import get_relationship_manager + +logger = get_logger("prompt") + + +def init_prompt(): + Prompt("你正在qq群里聊天，下面是群里在聊的内容：", "chat_target_group1") + Prompt("你正在和{sender_name}聊天，这是你们之前聊的内容：", "chat_target_private1") + Prompt("在群里聊天", "chat_target_group2") + Prompt("和{sender_name}私聊", "chat_target_private2") + + Prompt("\n你有以下这些**知识**：\n{prompt_info}\n请你**记住上面的知识**，之后可能会用到。\n", "knowledge_prompt") + Prompt("\n关于你们的关系，你需要知道：\n{relation_info}\n", "relation_prompt") + Prompt("你回想起了一些事情：\n{memory_info}\n", "memory_prompt") + + Prompt( + """{identity_block} + +{relation_info_block} +{memory_block} + +你现在的主要任务是和 {sender_name} 聊天。同时，也有其他用户会参与你们的聊天，你可以参考他们的回复内容，但是你主要还是关注你和{sender_name}的聊天内容。 + +{background_dialogue_prompt} +-------------------------------- +{time_block} +这是你和{sender_name}的对话，你们正在交流中： +{core_dialogue_prompt} + +对方最新发送的内容：{message_txt} +回复可以简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。 +不要输出多余内容(包括前后缀，冒号和引号，括号()，表情包，at或 @等 )。只输出回复内容，现在{sender_name}正在等待你的回复。 +你的回复风格不要浮夸，有逻辑和条理，请你继续回复{sender_name}。 +你的发言： +""", + "s4u_prompt", # New template for private CHAT chat + ) + + +class PromptBuilder: + def __init__(self): + self.prompt_built = "" + self.activate_messages = "" + + async def build_identity_block(self) -> str: + person_info_manager = get_person_info_manager() + bot_person_id = person_info_manager.get_person_id("system", "bot_id") + bot_name = global_config.bot.nickname + if global_config.bot.alias_names: + bot_nickname = f",也有人叫你{','.join(global_config.bot.alias_names)}" + else: + bot_nickname = "" + short_impression = await person_info_manager.get_value(bot_person_id, "short_impression") + try: + if isinstance(short_impression, str) and short_impression.strip(): + short_impression = ast.literal_eval(short_impression) + elif not short_impression: + logger.warning("short_impression为空，使用默认值") + short_impression = ["友好活泼", "人类"] + except (ValueError, SyntaxError) as e: + logger.error(f"解析short_impression失败: {e}, 原始值: {short_impression}") + short_impression = ["友好活泼", "人类"] + + if not isinstance(short_impression, list) or len(short_impression) < 2: + logger.warning(f"short_impression格式不正确: {short_impression}, 使用默认值") + short_impression = ["友好活泼", "人类"] + personality = short_impression[0] + identity = short_impression[1] + prompt_personality = personality + "，" + identity + return f"你的名字是{bot_name}{bot_nickname}，你{prompt_personality}：" + + async def build_relation_info(self, chat_stream) -> str: + is_group_chat = bool(chat_stream.group_info) + who_chat_in_group = [] + if is_group_chat: + who_chat_in_group = get_recent_group_speaker( + chat_stream.stream_id, + (chat_stream.user_info.platform, chat_stream.user_info.user_id) if chat_stream.user_info else None, + limit=global_config.chat.max_context_size, + ) + elif chat_stream.user_info: + who_chat_in_group.append( + (chat_stream.user_info.platform, chat_stream.user_info.user_id, chat_stream.user_info.user_nickname) + ) + + relation_prompt = "" + if global_config.relationship.enable_relationship and who_chat_in_group: + relationship_manager = get_relationship_manager() + relation_info_list = await asyncio.gather( + *[relationship_manager.build_relationship_info(person) for person in who_chat_in_group] + ) + relation_info = "".join(relation_info_list) + if relation_info: + relation_prompt = await global_prompt_manager.format_prompt( + "relation_prompt", relation_info=relation_info + ) + return relation_prompt + + async def build_memory_block(self, text: str) -> str: + related_memory = await hippocampus_manager.get_memory_from_text( + text=text, max_memory_num=2, max_memory_length=2, max_depth=3, fast_retrieval=False + ) + + related_memory_info = "" + if related_memory: + for memory in related_memory: + related_memory_info += memory[1] + return await global_prompt_manager.format_prompt("memory_prompt", memory_info=related_memory_info) + return "" + + def build_chat_history_prompts(self, chat_stream, message) -> (str, str): + message_list_before_now = get_raw_msg_before_timestamp_with_chat( + chat_id=chat_stream.stream_id, + timestamp=time.time(), + limit=100, + ) + + talk_type = message.message_info.platform + ":" + message.chat_stream.user_info.user_id + + core_dialogue_list = [] + background_dialogue_list = [] + bot_id = str(global_config.bot.qq_account) + target_user_id = str(message.chat_stream.user_info.user_id) + + for msg_dict in message_list_before_now: + try: + msg_user_id = str(msg_dict.get("user_id")) + if msg_user_id == bot_id: + if msg_dict.get("reply_to") and talk_type == msg_dict.get("reply_to"): + core_dialogue_list.append(msg_dict) + else: + background_dialogue_list.append(msg_dict) + elif msg_user_id == target_user_id: + core_dialogue_list.append(msg_dict) + else: + background_dialogue_list.append(msg_dict) + except Exception as e: + logger.error(f"无法处理历史消息记录: {msg_dict}, 错误: {e}") + + background_dialogue_prompt = "" + if background_dialogue_list: + latest_25_msgs = background_dialogue_list[-25:] + background_dialogue_prompt_str = build_readable_messages( + latest_25_msgs, + merge_messages=True, + timestamp_mode="normal_no_YMD", + show_pic=False, + ) + background_dialogue_prompt = f"这是其他用户的发言：\n{background_dialogue_prompt_str}" + + core_msg_str = "" + if core_dialogue_list: + core_dialogue_list = core_dialogue_list[-50:] + + first_msg = core_dialogue_list[0] + start_speaking_user_id = first_msg.get("user_id") + if start_speaking_user_id == bot_id: + last_speaking_user_id = bot_id + msg_seg_str = "你的发言：\n" + else: + start_speaking_user_id = target_user_id + last_speaking_user_id = start_speaking_user_id + msg_seg_str = "对方的发言：\n" + + msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(first_msg.get('time')))}: {first_msg.get('processed_plain_text')}\n" + + all_msg_seg_list = [] + for msg in core_dialogue_list[1:]: + speaker = msg.get("user_id") + if speaker == last_speaking_user_id: + msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.get('time')))}: {msg.get('processed_plain_text')}\n" + else: + msg_seg_str = f"{msg_seg_str}\n" + all_msg_seg_list.append(msg_seg_str) + + if speaker == bot_id: + msg_seg_str = "你的发言：\n" + else: + msg_seg_str = "对方的发言：\n" + + msg_seg_str += f"{time.strftime('%H:%M:%S', time.localtime(msg.get('time')))}: {msg.get('processed_plain_text')}\n" + last_speaking_user_id = speaker + + all_msg_seg_list.append(msg_seg_str) + for msg in all_msg_seg_list: + core_msg_str += msg + + return core_msg_str, background_dialogue_prompt + + async def build_prompt_normal( + self, + message, + chat_stream, + message_txt: str, + sender_name: str = "某人", + ) -> str: + identity_block, relation_info_block, memory_block = await asyncio.gather( + self.build_identity_block(), self.build_relation_info(chat_stream), self.build_memory_block(message_txt) + ) + + core_dialogue_prompt, background_dialogue_prompt = self.build_chat_history_prompts(chat_stream, message) + + time_block = f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" + + template_name = "s4u_prompt" + + prompt = await global_prompt_manager.format_prompt( + template_name, + identity_block=identity_block, + time_block=time_block, + relation_info_block=relation_info_block, + memory_block=memory_block, + sender_name=sender_name, + core_dialogue_prompt=core_dialogue_prompt, + background_dialogue_prompt=background_dialogue_prompt, + message_txt=message_txt, + ) + + return prompt + + +def weighted_sample_no_replacement(items, weights, k) -> list: + """ + 加权且不放回地随机抽取k个元素。 + + 参数： + items: 待抽取的元素列表 + weights: 每个元素对应的权重（与items等长，且为正数） + k: 需要抽取的元素个数 + 返回： + selected: 按权重加权且不重复抽取的k个元素组成的列表 + + 如果 items 中的元素不足 k 个，就只会返回所有可用的元素 + + 实现思路： + 每次从当前池中按权重加权随机选出一个元素，选中后将其从池中移除，重复k次。 + 这样保证了： + 1. count越大被选中概率越高 + 2. 不会重复选中同一个元素 + """ + selected = [] + pool = list(zip(items, weights)) + for _ in range(min(k, len(pool))): + total = sum(w for _, w in pool) + r = random.uniform(0, total) + upto = 0 + for idx, (item, weight) in enumerate(pool): + upto += weight + if upto >= r: + selected.append(item) + pool.pop(idx) + break + return selected + + +init_prompt() +prompt_builder = PromptBuilder() diff --git a/src/mais4u/mais4u_chat/s4u_stream_generator.py b/src/mais4u/mais4u_chat/s4u_stream_generator.py new file mode 100644 index 000000000..449922886 --- /dev/null +++ b/src/mais4u/mais4u_chat/s4u_stream_generator.py @@ -0,0 +1,157 @@ +import os +from typing import AsyncGenerator +from src.llm_models.utils_model import LLMRequest +from src.mais4u.openai_client import AsyncOpenAIClient +from src.config.config import global_config +from src.chat.message_receive.message import MessageRecv +from src.mais4u.mais4u_chat.s4u_prompt import prompt_builder +from src.common.logger import get_logger +from src.person_info.person_info import PersonInfoManager, get_person_info_manager +import asyncio +import re + + +logger = get_logger("s4u_stream_generator") + + +class S4UStreamGenerator: + def __init__(self): + replyer_1_config = global_config.model.replyer_1 + provider = replyer_1_config.get("provider") + if not provider: + logger.error("`replyer_1` 在配置文件中缺少 `provider` 字段") + raise ValueError("`replyer_1` 在配置文件中缺少 `provider` 字段") + + api_key = os.environ.get(f"{provider.upper()}_KEY") + base_url = os.environ.get(f"{provider.upper()}_BASE_URL") + + if not api_key: + logger.error(f"环境变量 {provider.upper()}_KEY 未设置") + raise ValueError(f"环境变量 {provider.upper()}_KEY 未设置") + + self.client_1 = AsyncOpenAIClient(api_key=api_key, base_url=base_url) + self.model_1_name = replyer_1_config.get("name") + if not self.model_1_name: + logger.error("`replyer_1` 在配置文件中缺少 `model_name` 字段") + raise ValueError("`replyer_1` 在配置文件中缺少 `model_name` 字段") + self.replyer_1_config = replyer_1_config + + self.model_sum = LLMRequest(model=global_config.model.memory_summary, temperature=0.7, request_type="relation") + self.current_model_name = "unknown model" + self.partial_response = "" + + # 正则表达式用于按句子切分，同时处理各种标点和边缘情况 + # 匹配常见的句子结束符，但会忽略引号内和数字中的标点 + self.sentence_split_pattern = re.compile( + r'([^\s\w"\'([{]*["\'([{].*?["\'}\])][^\s\w"\'([{]*|' # 匹配被引号/括号包裹的内容 + r'[^.。!?？！\n\r]+(?:[.。!?？！\n\r](?![\'"])|$))', # 匹配直到句子结束符 + re.UNICODE | re.DOTALL, + ) + + async def generate_response( + self, message: MessageRecv, previous_reply_context: str = "" + ) -> AsyncGenerator[str, None]: + """根据当前模型类型选择对应的生成函数""" + # 从global_config中获取模型概率值并选择模型 + self.partial_response = "" + current_client = self.client_1 + self.current_model_name = self.model_1_name + + person_id = PersonInfoManager.get_person_id( + message.chat_stream.user_info.platform, message.chat_stream.user_info.user_id + ) + person_info_manager = get_person_info_manager() + person_name = await person_info_manager.get_value(person_id, "person_name") + + if message.chat_stream.user_info.user_nickname: + sender_name = f"[{message.chat_stream.user_info.user_nickname}]（你叫ta{person_name}）" + else: + sender_name = f"用户({message.chat_stream.user_info.user_id})" + + # 构建prompt + if previous_reply_context: + message_txt = f""" + 你正在回复用户的消息，但中途被打断了。这是已有的对话上下文: + [你已经对上一条消息说的话]: {previous_reply_context} + --- + [这是用户发来的新消息, 你需要结合上下文，对此进行回复]: + {message.processed_plain_text} + """ + else: + message_txt = message.processed_plain_text + + prompt = await prompt_builder.build_prompt_normal( + message=message, + message_txt=message_txt, + sender_name=sender_name, + chat_stream=message.chat_stream, + ) + + logger.info( + f"{self.current_model_name}思考:{message_txt[:30] + '...' if len(message_txt) > 30 else message_txt}" + ) # noqa: E501 + + extra_kwargs = {} + if self.replyer_1_config.get("enable_thinking") is not None: + extra_kwargs["enable_thinking"] = self.replyer_1_config.get("enable_thinking") + if self.replyer_1_config.get("thinking_budget") is not None: + extra_kwargs["thinking_budget"] = self.replyer_1_config.get("thinking_budget") + + async for chunk in self._generate_response_with_model( + prompt, current_client, self.current_model_name, **extra_kwargs + ): + yield chunk + + async def _generate_response_with_model( + self, + prompt: str, + client: AsyncOpenAIClient, + model_name: str, + **kwargs, + ) -> AsyncGenerator[str, None]: + print(prompt) + + buffer = "" + delimiters = "，。！？,.!?\n\r" # For final trimming + punctuation_buffer = "" + + async for content in client.get_stream_content( + messages=[{"role": "user", "content": prompt}], model=model_name, **kwargs + ): + buffer += content + + # 使用正则表达式匹配句子 + last_match_end = 0 + for match in self.sentence_split_pattern.finditer(buffer): + sentence = match.group(0).strip() + if sentence: + # 如果句子看起来完整（即不只是等待更多内容），则发送 + if match.end(0) < len(buffer) or sentence.endswith(tuple(delimiters)): + # 检查是否只是一个标点符号 + if sentence in [",", "，", ".", "。", "!", "！", "?", "？"]: + punctuation_buffer += sentence + else: + # 发送之前累积的标点和当前句子 + to_yield = punctuation_buffer + sentence + if to_yield.endswith((",", "，")): + to_yield = to_yield.rstrip(",，") + + self.partial_response += to_yield + yield to_yield + punctuation_buffer = "" # 清空标点符号缓冲区 + await asyncio.sleep(0) # 允许其他任务运行 + + last_match_end = match.end(0) + + # 从缓冲区移除已发送的部分 + if last_match_end > 0: + buffer = buffer[last_match_end:] + + # 发送缓冲区中剩余的任何内容 + to_yield = (punctuation_buffer + buffer).strip() + if to_yield: + if to_yield.endswith(("，", ",")): + to_yield = to_yield.rstrip("，,") + if to_yield: + self.partial_response += to_yield + yield to_yield diff --git a/src/mais4u/openai_client.py b/src/mais4u/openai_client.py new file mode 100644 index 000000000..2a5873dec --- /dev/null +++ b/src/mais4u/openai_client.py @@ -0,0 +1,286 @@ +from typing import AsyncGenerator, Dict, List, Optional, Union +from dataclasses import dataclass +from openai import AsyncOpenAI +from openai.types.chat import ChatCompletion, ChatCompletionChunk + + +@dataclass +class ChatMessage: + """聊天消息数据类""" + + role: str + content: str + + def to_dict(self) -> Dict[str, str]: + return {"role": self.role, "content": self.content} + + +class AsyncOpenAIClient: + """异步OpenAI客户端，支持流式传输""" + + def __init__(self, api_key: str, base_url: Optional[str] = None): + """ + 初始化客户端 + + Args: + api_key: OpenAI API密钥 + base_url: 可选的API基础URL，用于自定义端点 + """ + self.client = AsyncOpenAI( + api_key=api_key, + base_url=base_url, + timeout=10.0, # 设置60秒的全局超时 + ) + + async def chat_completion( + self, + messages: List[Union[ChatMessage, Dict[str, str]]], + model: str = "gpt-3.5-turbo", + temperature: float = 0.7, + max_tokens: Optional[int] = None, + **kwargs, + ) -> ChatCompletion: + """ + 非流式聊天完成 + + Args: + messages: 消息列表 + model: 模型名称 + temperature: 温度参数 + max_tokens: 最大token数 + **kwargs: 其他参数 + + Returns: + 完整的聊天回复 + """ + # 转换消息格式 + formatted_messages = [] + for msg in messages: + if isinstance(msg, ChatMessage): + formatted_messages.append(msg.to_dict()) + else: + formatted_messages.append(msg) + + extra_body = {} + if kwargs.get("enable_thinking") is not None: + extra_body["enable_thinking"] = kwargs.pop("enable_thinking") + if kwargs.get("thinking_budget") is not None: + extra_body["thinking_budget"] = kwargs.pop("thinking_budget") + + response = await self.client.chat.completions.create( + model=model, + messages=formatted_messages, + temperature=temperature, + max_tokens=max_tokens, + stream=False, + extra_body=extra_body if extra_body else None, + **kwargs, + ) + + return response + + async def chat_completion_stream( + self, + messages: List[Union[ChatMessage, Dict[str, str]]], + model: str = "gpt-3.5-turbo", + temperature: float = 0.7, + max_tokens: Optional[int] = None, + **kwargs, + ) -> AsyncGenerator[ChatCompletionChunk, None]: + """ + 流式聊天完成 + + Args: + messages: 消息列表 + model: 模型名称 + temperature: 温度参数 + max_tokens: 最大token数 + **kwargs: 其他参数 + + Yields: + ChatCompletionChunk: 流式响应块 + """ + # 转换消息格式 + formatted_messages = [] + for msg in messages: + if isinstance(msg, ChatMessage): + formatted_messages.append(msg.to_dict()) + else: + formatted_messages.append(msg) + + extra_body = {} + if kwargs.get("enable_thinking") is not None: + extra_body["enable_thinking"] = kwargs.pop("enable_thinking") + if kwargs.get("thinking_budget") is not None: + extra_body["thinking_budget"] = kwargs.pop("thinking_budget") + + stream = await self.client.chat.completions.create( + model=model, + messages=formatted_messages, + temperature=temperature, + max_tokens=max_tokens, + stream=True, + extra_body=extra_body if extra_body else None, + **kwargs, + ) + + async for chunk in stream: + yield chunk + + async def get_stream_content( + self, + messages: List[Union[ChatMessage, Dict[str, str]]], + model: str = "gpt-3.5-turbo", + temperature: float = 0.7, + max_tokens: Optional[int] = None, + **kwargs, + ) -> AsyncGenerator[str, None]: + """ + 获取流式内容（只返回文本内容） + + Args: + messages: 消息列表 + model: 模型名称 + temperature: 温度参数 + max_tokens: 最大token数 + **kwargs: 其他参数 + + Yields: + str: 文本内容片段 + """ + async for chunk in self.chat_completion_stream( + messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, **kwargs + ): + if chunk.choices and chunk.choices[0].delta.content: + yield chunk.choices[0].delta.content + + async def collect_stream_response( + self, + messages: List[Union[ChatMessage, Dict[str, str]]], + model: str = "gpt-3.5-turbo", + temperature: float = 0.7, + max_tokens: Optional[int] = None, + **kwargs, + ) -> str: + """ + 收集完整的流式响应 + + Args: + messages: 消息列表 + model: 模型名称 + temperature: 温度参数 + max_tokens: 最大token数 + **kwargs: 其他参数 + + Returns: + str: 完整的响应文本 + """ + full_response = "" + async for content in self.get_stream_content( + messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, **kwargs + ): + full_response += content + + return full_response + + async def close(self): + """关闭客户端""" + await self.client.close() + + async def __aenter__(self): + """异步上下文管理器入口""" + return self + + async def __aexit__(self, exc_type, exc_val, exc_tb): + """异步上下文管理器退出""" + await self.close() + + +class ConversationManager: + """对话管理器，用于管理对话历史""" + + def __init__(self, client: AsyncOpenAIClient, system_prompt: Optional[str] = None): + """ + 初始化对话管理器 + + Args: + client: OpenAI客户端实例 + system_prompt: 系统提示词 + """ + self.client = client + self.messages: List[ChatMessage] = [] + + if system_prompt: + self.messages.append(ChatMessage(role="system", content=system_prompt)) + + def add_user_message(self, content: str): + """添加用户消息""" + self.messages.append(ChatMessage(role="user", content=content)) + + def add_assistant_message(self, content: str): + """添加助手消息""" + self.messages.append(ChatMessage(role="assistant", content=content)) + + async def send_message_stream( + self, content: str, model: str = "gpt-3.5-turbo", **kwargs + ) -> AsyncGenerator[str, None]: + """ + 发送消息并获取流式响应 + + Args: + content: 用户消息内容 + model: 模型名称 + **kwargs: 其他参数 + + Yields: + str: 响应内容片段 + """ + self.add_user_message(content) + + response_content = "" + async for chunk in self.client.get_stream_content(messages=self.messages, model=model, **kwargs): + response_content += chunk + yield chunk + + self.add_assistant_message(response_content) + + async def send_message(self, content: str, model: str = "gpt-3.5-turbo", **kwargs) -> str: + """ + 发送消息并获取完整响应 + + Args: + content: 用户消息内容 + model: 模型名称 + **kwargs: 其他参数 + + Returns: + str: 完整响应 + """ + self.add_user_message(content) + + response = await self.client.chat_completion(messages=self.messages, model=model, **kwargs) + + response_content = response.choices[0].message.content + self.add_assistant_message(response_content) + + return response_content + + def clear_history(self, keep_system: bool = True): + """ + 清除对话历史 + + Args: + keep_system: 是否保留系统消息 + """ + if keep_system and self.messages and self.messages[0].role == "system": + self.messages = [self.messages[0]] + else: + self.messages = [] + + def get_message_count(self) -> int: + """获取消息数量""" + return len(self.messages) + + def get_conversation_history(self) -> List[Dict[str, str]]: + """获取对话历史""" + return [msg.to_dict() for msg in self.messages] diff --git a/src/person_info/relationship_builder.py b/src/person_info/relationship_builder.py new file mode 100644 index 000000000..11d7e5b47 --- /dev/null +++ b/src/person_info/relationship_builder.py @@ -0,0 +1,465 @@ +import time +import traceback +import os +import pickle +from typing import List, Dict +from src.config.config import global_config +from src.common.logger import get_logger +from src.chat.message_receive.chat_stream import get_chat_manager +from src.person_info.relationship_manager import get_relationship_manager +from src.person_info.person_info import get_person_info_manager, PersonInfoManager +from src.chat.utils.chat_message_builder import ( + get_raw_msg_by_timestamp_with_chat, + get_raw_msg_by_timestamp_with_chat_inclusive, + get_raw_msg_before_timestamp_with_chat, + num_new_messages_since, +) + +logger = get_logger("relationship_builder") + +# 消息段清理配置 +SEGMENT_CLEANUP_CONFIG = { + "enable_cleanup": True, # 是否启用清理 + "max_segment_age_days": 7, # 消息段最大保存天数 + "max_segments_per_user": 10, # 每用户最大消息段数 + "cleanup_interval_hours": 1, # 清理间隔（小时） +} + + +class RelationshipBuilder: + """关系构建器 + + 独立运行的关系构建类，基于特定的chat_id进行工作 + 负责跟踪用户消息活动、管理消息段、触发关系构建和印象更新 + """ + + def __init__(self, chat_id: str): + """初始化关系构建器 + + Args: + chat_id: 聊天ID + """ + self.chat_id = chat_id + # 新的消息段缓存结构： + # {person_id: [{"start_time": float, "end_time": float, "last_msg_time": float, "message_count": int}, ...]} + self.person_engaged_cache: Dict[str, List[Dict[str, any]]] = {} + + # 持久化存储文件路径 + self.cache_file_path = os.path.join("data", "relationship", f"relationship_cache_{self.chat_id}.pkl") + + # 最后处理的消息时间，避免重复处理相同消息 + current_time = time.time() + self.last_processed_message_time = current_time + + # 最后清理时间，用于定期清理老消息段 + self.last_cleanup_time = 0.0 + + # 获取聊天名称用于日志 + try: + chat_name = get_chat_manager().get_stream_name(self.chat_id) + self.log_prefix = f"[{chat_name}] 关系构建" + except Exception: + self.log_prefix = f"[{self.chat_id}] 关系构建" + + # 加载持久化的缓存 + self._load_cache() + + # ================================ + # 缓存管理模块 + # 负责持久化存储、状态管理、缓存读写 + # ================================ + + def _load_cache(self): + """从文件加载持久化的缓存""" + if os.path.exists(self.cache_file_path): + try: + with open(self.cache_file_path, "rb") as f: + cache_data = pickle.load(f) + # 新格式：包含额外信息的缓存 + self.person_engaged_cache = cache_data.get("person_engaged_cache", {}) + self.last_processed_message_time = cache_data.get("last_processed_message_time", 0.0) + self.last_cleanup_time = cache_data.get("last_cleanup_time", 0.0) + + logger.info( + f"{self.log_prefix} 成功加载关系缓存，包含 {len(self.person_engaged_cache)} 个用户，最后处理时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.last_processed_message_time)) if self.last_processed_message_time > 0 else '未设置'}" + ) + except Exception as e: + logger.error(f"{self.log_prefix} 加载关系缓存失败: {e}") + self.person_engaged_cache = {} + self.last_processed_message_time = 0.0 + else: + logger.info(f"{self.log_prefix} 关系缓存文件不存在，使用空缓存") + + def _save_cache(self): + """保存缓存到文件""" + try: + os.makedirs(os.path.dirname(self.cache_file_path), exist_ok=True) + cache_data = { + "person_engaged_cache": self.person_engaged_cache, + "last_processed_message_time": self.last_processed_message_time, + "last_cleanup_time": self.last_cleanup_time, + } + with open(self.cache_file_path, "wb") as f: + pickle.dump(cache_data, f) + logger.debug(f"{self.log_prefix} 成功保存关系缓存") + except Exception as e: + logger.error(f"{self.log_prefix} 保存关系缓存失败: {e}") + + # ================================ + # 消息段管理模块 + # 负责跟踪用户消息活动、管理消息段、清理过期数据 + # ================================ + + def _update_message_segments(self, person_id: str, message_time: float): + """更新用户的消息段 + + Args: + person_id: 用户ID + message_time: 消息时间戳 + """ + if person_id not in self.person_engaged_cache: + self.person_engaged_cache[person_id] = [] + + segments = self.person_engaged_cache[person_id] + + # 获取该消息前5条消息的时间作为潜在的开始时间 + before_messages = get_raw_msg_before_timestamp_with_chat(self.chat_id, message_time, limit=5) + if before_messages: + potential_start_time = before_messages[0]["time"] + else: + potential_start_time = message_time + + # 如果没有现有消息段，创建新的 + if not segments: + new_segment = { + "start_time": potential_start_time, + "end_time": message_time, + "last_msg_time": message_time, + "message_count": self._count_messages_in_timerange(potential_start_time, message_time), + } + segments.append(new_segment) + + person_name = get_person_info_manager().get_value_sync(person_id, "person_name") or person_id + logger.info( + f"{self.log_prefix} 眼熟用户 {person_name} 在 {time.strftime('%H:%M:%S', time.localtime(potential_start_time))} - {time.strftime('%H:%M:%S', time.localtime(message_time))} 之间有 {new_segment['message_count']} 条消息" + ) + self._save_cache() + return + + # 获取最后一个消息段 + last_segment = segments[-1] + + # 计算从最后一条消息到当前消息之间的消息数量（不包含边界） + messages_between = self._count_messages_between(last_segment["last_msg_time"], message_time) + + if messages_between <= 10: + # 在10条消息内，延伸当前消息段 + last_segment["end_time"] = message_time + last_segment["last_msg_time"] = message_time + # 重新计算整个消息段的消息数量 + last_segment["message_count"] = self._count_messages_in_timerange( + last_segment["start_time"], last_segment["end_time"] + ) + logger.debug(f"{self.log_prefix} 延伸用户 {person_id} 的消息段: {last_segment}") + else: + # 超过10条消息，结束当前消息段并创建新的 + # 结束当前消息段：延伸到原消息段最后一条消息后5条消息的时间 + current_time = time.time() + after_messages = get_raw_msg_by_timestamp_with_chat( + self.chat_id, last_segment["last_msg_time"], current_time, limit=5, limit_mode="earliest" + ) + if after_messages and len(after_messages) >= 5: + # 如果有足够的后续消息，使用第5条消息的时间作为结束时间 + last_segment["end_time"] = after_messages[4]["time"] + + # 重新计算当前消息段的消息数量 + last_segment["message_count"] = self._count_messages_in_timerange( + last_segment["start_time"], last_segment["end_time"] + ) + + # 创建新的消息段 + new_segment = { + "start_time": potential_start_time, + "end_time": message_time, + "last_msg_time": message_time, + "message_count": self._count_messages_in_timerange(potential_start_time, message_time), + } + segments.append(new_segment) + person_info_manager = get_person_info_manager() + person_name = person_info_manager.get_value_sync(person_id, "person_name") or person_id + logger.info(f"{self.log_prefix} 重新眼熟用户 {person_name} 创建新消息段（超过10条消息间隔）: {new_segment}") + + self._save_cache() + + def _count_messages_in_timerange(self, start_time: float, end_time: float) -> int: + """计算指定时间范围内的消息数量（包含边界）""" + messages = get_raw_msg_by_timestamp_with_chat_inclusive(self.chat_id, start_time, end_time) + return len(messages) + + def _count_messages_between(self, start_time: float, end_time: float) -> int: + """计算两个时间点之间的消息数量（不包含边界），用于间隔检查""" + return num_new_messages_since(self.chat_id, start_time, end_time) + + def _get_total_message_count(self, person_id: str) -> int: + """获取用户所有消息段的总消息数量""" + if person_id not in self.person_engaged_cache: + return 0 + + total_count = 0 + for segment in self.person_engaged_cache[person_id]: + total_count += segment["message_count"] + + return total_count + + def _cleanup_old_segments(self) -> bool: + """清理老旧的消息段""" + if not SEGMENT_CLEANUP_CONFIG["enable_cleanup"]: + return False + + current_time = time.time() + + # 检查是否需要执行清理（基于时间间隔） + cleanup_interval_seconds = SEGMENT_CLEANUP_CONFIG["cleanup_interval_hours"] * 3600 + if current_time - self.last_cleanup_time < cleanup_interval_seconds: + return False + + logger.info(f"{self.log_prefix} 开始执行老消息段清理...") + + cleanup_stats = { + "users_cleaned": 0, + "segments_removed": 0, + "total_segments_before": 0, + "total_segments_after": 0, + } + + max_age_seconds = SEGMENT_CLEANUP_CONFIG["max_segment_age_days"] * 24 * 3600 + max_segments_per_user = SEGMENT_CLEANUP_CONFIG["max_segments_per_user"] + + users_to_remove = [] + + for person_id, segments in self.person_engaged_cache.items(): + cleanup_stats["total_segments_before"] += len(segments) + original_segment_count = len(segments) + + # 1. 按时间清理：移除过期的消息段 + segments_after_age_cleanup = [] + for segment in segments: + segment_age = current_time - segment["end_time"] + if segment_age <= max_age_seconds: + segments_after_age_cleanup.append(segment) + else: + cleanup_stats["segments_removed"] += 1 + logger.debug( + f"{self.log_prefix} 移除用户 {person_id} 的过期消息段: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(segment['start_time']))} - {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(segment['end_time']))}" + ) + + # 2. 按数量清理：如果消息段数量仍然过多，保留最新的 + if len(segments_after_age_cleanup) > max_segments_per_user: + # 按end_time排序，保留最新的 + segments_after_age_cleanup.sort(key=lambda x: x["end_time"], reverse=True) + segments_removed_count = len(segments_after_age_cleanup) - max_segments_per_user + cleanup_stats["segments_removed"] += segments_removed_count + segments_after_age_cleanup = segments_after_age_cleanup[:max_segments_per_user] + logger.debug( + f"{self.log_prefix} 用户 {person_id} 消息段数量过多，移除 {segments_removed_count} 个最老的消息段" + ) + + # 更新缓存 + if len(segments_after_age_cleanup) == 0: + # 如果没有剩余消息段，标记用户为待移除 + users_to_remove.append(person_id) + else: + self.person_engaged_cache[person_id] = segments_after_age_cleanup + cleanup_stats["total_segments_after"] += len(segments_after_age_cleanup) + + if original_segment_count != len(segments_after_age_cleanup): + cleanup_stats["users_cleaned"] += 1 + + # 移除没有消息段的用户 + for person_id in users_to_remove: + del self.person_engaged_cache[person_id] + logger.debug(f"{self.log_prefix} 移除用户 {person_id}：没有剩余消息段") + + # 更新最后清理时间 + self.last_cleanup_time = current_time + + # 保存缓存 + if cleanup_stats["segments_removed"] > 0 or len(users_to_remove) > 0: + self._save_cache() + logger.info( + f"{self.log_prefix} 清理完成 - 影响用户: {cleanup_stats['users_cleaned']}, 移除消息段: {cleanup_stats['segments_removed']}, 移除用户: {len(users_to_remove)}" + ) + logger.info( + f"{self.log_prefix} 消息段统计 - 清理前: {cleanup_stats['total_segments_before']}, 清理后: {cleanup_stats['total_segments_after']}" + ) + else: + logger.debug(f"{self.log_prefix} 清理完成 - 无需清理任何内容") + + return cleanup_stats["segments_removed"] > 0 or len(users_to_remove) > 0 + + def force_cleanup_user_segments(self, person_id: str) -> bool: + """强制清理指定用户的所有消息段""" + if person_id in self.person_engaged_cache: + segments_count = len(self.person_engaged_cache[person_id]) + del self.person_engaged_cache[person_id] + self._save_cache() + logger.info(f"{self.log_prefix} 强制清理用户 {person_id} 的 {segments_count} 个消息段") + return True + return False + + def get_cache_status(self) -> str: + """获取缓存状态信息，用于调试和监控""" + if not self.person_engaged_cache: + return f"{self.log_prefix} 关系缓存为空" + + status_lines = [f"{self.log_prefix} 关系缓存状态："] + status_lines.append( + f"最后处理消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.last_processed_message_time)) if self.last_processed_message_time > 0 else '未设置'}" + ) + status_lines.append( + f"最后清理时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(self.last_cleanup_time)) if self.last_cleanup_time > 0 else '未执行'}" + ) + status_lines.append(f"总用户数：{len(self.person_engaged_cache)}") + status_lines.append( + f"清理配置：{'启用' if SEGMENT_CLEANUP_CONFIG['enable_cleanup'] else '禁用'} (最大保存{SEGMENT_CLEANUP_CONFIG['max_segment_age_days']}天, 每用户最多{SEGMENT_CLEANUP_CONFIG['max_segments_per_user']}段)" + ) + status_lines.append("") + + for person_id, segments in self.person_engaged_cache.items(): + total_count = self._get_total_message_count(person_id) + status_lines.append(f"用户 {person_id}:") + status_lines.append(f" 总消息数：{total_count} ({total_count}/45)") + status_lines.append(f" 消息段数：{len(segments)}") + + for i, segment in enumerate(segments): + start_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(segment["start_time"])) + end_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(segment["end_time"])) + last_str = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(segment["last_msg_time"])) + status_lines.append( + f" 段{i + 1}: {start_str} -> {end_str} (最后消息: {last_str}, 消息数: {segment['message_count']})" + ) + status_lines.append("") + + return "\n".join(status_lines) + + # ================================ + # 主要处理流程 + # 统筹各模块协作、对外提供服务接口 + # ================================ + + async def build_relation(self): + """构建关系""" + self._cleanup_old_segments() + current_time = time.time() + + latest_messages = get_raw_msg_by_timestamp_with_chat( + self.chat_id, + self.last_processed_message_time, + current_time, + limit=50, # 获取自上次处理后的消息 + ) + if latest_messages: + # 处理所有新的非bot消息 + for latest_msg in latest_messages: + user_id = latest_msg.get("user_id") + platform = latest_msg.get("user_platform") or latest_msg.get("chat_info_platform") + msg_time = latest_msg.get("time", 0) + + if ( + user_id + and platform + and user_id != global_config.bot.qq_account + and msg_time > self.last_processed_message_time + ): + person_id = PersonInfoManager.get_person_id(platform, user_id) + self._update_message_segments(person_id, msg_time) + logger.debug( + f"{self.log_prefix} 更新用户 {person_id} 的消息段，消息时间：{time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(msg_time))}" + ) + self.last_processed_message_time = max(self.last_processed_message_time, msg_time) + + # 1. 检查是否有用户达到关系构建条件（总消息数达到45条） + users_to_build_relationship = [] + for person_id, segments in self.person_engaged_cache.items(): + total_message_count = self._get_total_message_count(person_id) + if total_message_count >= 45: + users_to_build_relationship.append(person_id) + logger.info( + f"{self.log_prefix} 用户 {person_id} 满足关系构建条件，总消息数：{total_message_count}，消息段数：{len(segments)}" + ) + elif total_message_count > 0: + # 记录进度信息 + logger.debug( + f"{self.log_prefix} 用户 {person_id} 进度：{total_message_count}/45 条消息，{len(segments)} 个消息段" + ) + + # 2. 为满足条件的用户构建关系 + for person_id in users_to_build_relationship: + segments = self.person_engaged_cache[person_id] + # 异步执行关系构建 + import asyncio + + asyncio.create_task(self.update_impression_on_segments(person_id, self.chat_id, segments)) + # 移除已处理的用户缓存 + del self.person_engaged_cache[person_id] + self._save_cache() + + # ================================ + # 关系构建模块 + # 负责触发关系构建、整合消息段、更新用户印象 + # ================================ + + async def update_impression_on_segments(self, person_id: str, chat_id: str, segments: List[Dict[str, any]]): + """基于消息段更新用户印象""" + logger.debug(f"开始为 {person_id} 基于 {len(segments)} 个消息段更新印象") + try: + processed_messages = [] + + for i, segment in enumerate(segments): + start_time = segment["start_time"] + end_time = segment["end_time"] + start_date = time.strftime("%Y-%m-%d %H:%M", time.localtime(start_time)) + + # 获取该段的消息（包含边界） + segment_messages = get_raw_msg_by_timestamp_with_chat_inclusive(self.chat_id, start_time, end_time) + logger.info( + f"消息段 {i + 1}: {start_date} - {time.strftime('%Y-%m-%d %H:%M', time.localtime(end_time))}, 消息数: {len(segment_messages)}" + ) + + if segment_messages: + # 如果不是第一个消息段，在消息列表前添加间隔标识 + if i > 0: + # 创建一个特殊的间隔消息 + gap_message = { + "time": start_time - 0.1, # 稍微早于段开始时间 + "user_id": "system", + "user_platform": "system", + "user_nickname": "系统", + "user_cardname": "", + "display_message": f"...（中间省略一些消息）{start_date} 之后的消息如下...", + "is_action_record": True, + "chat_info_platform": segment_messages[0].get("chat_info_platform", ""), + "chat_id": chat_id, + } + processed_messages.append(gap_message) + + # 添加该段的所有消息 + processed_messages.extend(segment_messages) + + if processed_messages: + # 按时间排序所有消息（包括间隔标识） + processed_messages.sort(key=lambda x: x["time"]) + + logger.info(f"为 {person_id} 获取到总共 {len(processed_messages)} 条消息（包含间隔标识）用于印象更新") + relationship_manager = get_relationship_manager() + + # 调用原有的更新方法 + await relationship_manager.update_person_impression( + person_id=person_id, timestamp=time.time(), bot_engaged_messages=processed_messages + ) + else: + logger.info(f"没有找到 {person_id} 的消息段对应的消息，不更新印象") + + except Exception as e: + logger.error(f"为 {person_id} 更新印象时发生错误: {e}") + logger.error(traceback.format_exc()) diff --git a/src/person_info/relationship_builder_manager.py b/src/person_info/relationship_builder_manager.py new file mode 100644 index 000000000..ce8d254e0 --- /dev/null +++ b/src/person_info/relationship_builder_manager.py @@ -0,0 +1,103 @@ +from typing import Dict, Optional, List +from src.common.logger import get_logger +from .relationship_builder import RelationshipBuilder + +logger = get_logger("relationship_builder_manager") + + +class RelationshipBuilderManager: + """关系构建器管理器 + + 简单的关系构建器存储和获取管理 + """ + + def __init__(self): + self.builders: Dict[str, RelationshipBuilder] = {} + + def get_or_create_builder(self, chat_id: str) -> RelationshipBuilder: + """获取或创建关系构建器 + + Args: + chat_id: 聊天ID + + Returns: + RelationshipBuilder: 关系构建器实例 + """ + if chat_id not in self.builders: + self.builders[chat_id] = RelationshipBuilder(chat_id) + logger.info(f"创建聊天 {chat_id} 的关系构建器") + + return self.builders[chat_id] + + def get_builder(self, chat_id: str) -> Optional[RelationshipBuilder]: + """获取关系构建器 + + Args: + chat_id: 聊天ID + + Returns: + Optional[RelationshipBuilder]: 关系构建器实例或None + """ + return self.builders.get(chat_id) + + def remove_builder(self, chat_id: str) -> bool: + """移除关系构建器 + + Args: + chat_id: 聊天ID + + Returns: + bool: 是否成功移除 + """ + if chat_id in self.builders: + del self.builders[chat_id] + logger.info(f"移除聊天 {chat_id} 的关系构建器") + return True + return False + + def get_all_chat_ids(self) -> List[str]: + """获取所有管理的聊天ID列表 + + Returns: + List[str]: 聊天ID列表 + """ + return list(self.builders.keys()) + + def get_status(self) -> Dict[str, any]: + """获取管理器状态 + + Returns: + Dict[str, any]: 状态信息 + """ + return { + "total_builders": len(self.builders), + "chat_ids": list(self.builders.keys()), + } + + async def process_chat_messages(self, chat_id: str): + """处理指定聊天的消息 + + Args: + chat_id: 聊天ID + """ + builder = self.get_or_create_builder(chat_id) + await builder.build_relation() + + async def force_cleanup_user(self, chat_id: str, person_id: str) -> bool: + """强制清理指定用户的关系构建缓存 + + Args: + chat_id: 聊天ID + person_id: 用户ID + + Returns: + bool: 是否成功清理 + """ + builder = self.get_builder(chat_id) + if builder: + return builder.force_cleanup_user_segments(person_id) + return False + + +# 全局管理器实例 +relationship_builder_manager = RelationshipBuilderManager() diff --git a/src/person_info/relationship_fetcher.py b/src/person_info/relationship_fetcher.py new file mode 100644 index 000000000..15bc6cc81 --- /dev/null +++ b/src/person_info/relationship_fetcher.py @@ -0,0 +1,449 @@ +from src.config.config import global_config +from src.llm_models.utils_model import LLMRequest +import time +import traceback +from src.common.logger import get_logger +from src.chat.utils.prompt_builder import Prompt, global_prompt_manager +from src.person_info.person_info import get_person_info_manager +from typing import List, Dict +from json_repair import repair_json +from src.chat.message_receive.chat_stream import get_chat_manager +import json + + +logger = get_logger("relationship_fetcher") + + +def init_real_time_info_prompts(): + """初始化实时信息提取相关的提示词""" + relationship_prompt = """ +<聊天记录> +{chat_observe_info} + + +{name_block} +现在，你想要回复{person_name}的消息，消息内容是：{target_message}。请根据聊天记录和你要回复的消息，从你对{person_name}的了解中提取有关的信息： +1.你需要提供你想要提取的信息具体是哪方面的信息，例如：年龄，性别，你们之间的交流方式，最近发生的事等等。 +2.请注意，请不要重复调取相同的信息，已经调取的信息如下： +{info_cache_block} +3.如果当前聊天记录中没有需要查询的信息，或者现有信息已经足够回复，请返回{{"none": "不需要查询"}} + +请以json格式输出，例如： + +{{ + "info_type": "信息类型", +}} + +请严格按照json输出格式，不要输出多余内容： +""" + Prompt(relationship_prompt, "real_time_info_identify_prompt") + + fetch_info_prompt = """ + +{name_block} +以下是你在之前与{person_name}的交流中，产生的对{person_name}的了解： +{person_impression_block} +{points_text_block} + +请从中提取用户"{person_name}"的有关"{info_type}"信息 +请以json格式输出，例如： + +{{ + {info_json_str} +}} + +请严格按照json输出格式，不要输出多余内容： +""" + Prompt(fetch_info_prompt, "real_time_fetch_person_info_prompt") + + +class RelationshipFetcher: + def __init__(self, chat_id): + self.chat_id = chat_id + + # 信息获取缓存：记录正在获取的信息请求 + self.info_fetching_cache: List[Dict[str, any]] = [] + + # 信息结果缓存：存储已获取的信息结果，带TTL + self.info_fetched_cache: Dict[str, Dict[str, any]] = {} + # 结构：{person_id: {info_type: {"info": str, "ttl": int, "start_time": float, "person_name": str, "unknow": bool}}} + + # LLM模型配置 + self.llm_model = LLMRequest( + model=global_config.model.relation, + request_type="relation", + ) + + # 小模型用于即时信息提取 + self.instant_llm_model = LLMRequest( + model=global_config.model.utils_small, + request_type="relation.instant", + ) + + name = get_chat_manager().get_stream_name(self.chat_id) + self.log_prefix = f"[{name}] 实时信息" + + def _cleanup_expired_cache(self): + """清理过期的信息缓存""" + for person_id in list(self.info_fetched_cache.keys()): + for info_type in list(self.info_fetched_cache[person_id].keys()): + self.info_fetched_cache[person_id][info_type]["ttl"] -= 1 + if self.info_fetched_cache[person_id][info_type]["ttl"] <= 0: + del self.info_fetched_cache[person_id][info_type] + if not self.info_fetched_cache[person_id]: + del self.info_fetched_cache[person_id] + + async def build_relation_info(self, person_id, target_message, chat_history): + # 清理过期的信息缓存 + self._cleanup_expired_cache() + + person_info_manager = get_person_info_manager() + person_name = await person_info_manager.get_value(person_id, "person_name") + short_impression = await person_info_manager.get_value(person_id, "short_impression") + + info_type = await self._build_fetch_query(person_id, target_message, chat_history) + if info_type: + await self._extract_single_info(person_id, info_type, person_name) + + relation_info = self._organize_known_info() + relation_info = f"你对{person_name}的印象是：{short_impression}\n{relation_info}" + return relation_info + + async def _build_fetch_query(self, person_id, target_message, chat_history): + nickname_str = ",".join(global_config.bot.alias_names) + name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" + person_info_manager = get_person_info_manager() + person_name = await person_info_manager.get_value(person_id, "person_name") + + info_cache_block = self._build_info_cache_block() + + prompt = (await global_prompt_manager.get_prompt_async("real_time_info_identify_prompt")).format( + chat_observe_info=chat_history, + name_block=name_block, + info_cache_block=info_cache_block, + person_name=person_name, + target_message=target_message, + ) + + try: + logger.debug(f"{self.log_prefix} 信息识别prompt: \n{prompt}\n") + content, _ = await self.llm_model.generate_response_async(prompt=prompt) + + if content: + content_json = json.loads(repair_json(content)) + + # 检查是否返回了不需要查询的标志 + if "none" in content_json: + logger.info(f"{self.log_prefix} LLM判断当前不需要查询任何信息：{content_json.get('none', '')}") + return None + + info_type = content_json.get("info_type") + if info_type: + # 记录信息获取请求 + self.info_fetching_cache.append( + { + "person_id": get_person_info_manager().get_person_id_by_person_name(person_name), + "person_name": person_name, + "info_type": info_type, + "start_time": time.time(), + "forget": False, + } + ) + + # 限制缓存大小 + if len(self.info_fetching_cache) > 10: + self.info_fetching_cache.pop(0) + + logger.info(f"{self.log_prefix} 识别到需要调取用户 {person_name} 的[{info_type}]信息") + return info_type + else: + logger.warning(f"{self.log_prefix} LLM未返回有效的info_type。响应: {content}") + + except Exception as e: + logger.error(f"{self.log_prefix} 执行信息识别LLM请求时出错: {e}") + logger.error(traceback.format_exc()) + + return None + + def _build_info_cache_block(self) -> str: + """构建已获取信息的缓存块""" + info_cache_block = "" + if self.info_fetching_cache: + # 对于每个(person_id, info_type)组合，只保留最新的记录 + latest_records = {} + for info_fetching in self.info_fetching_cache: + key = (info_fetching["person_id"], info_fetching["info_type"]) + if key not in latest_records or info_fetching["start_time"] > latest_records[key]["start_time"]: + latest_records[key] = info_fetching + + # 按时间排序并生成显示文本 + sorted_records = sorted(latest_records.values(), key=lambda x: x["start_time"]) + for info_fetching in sorted_records: + info_cache_block += ( + f"你已经调取了[{info_fetching['person_name']}]的[{info_fetching['info_type']}]信息\n" + ) + return info_cache_block + + async def _extract_single_info(self, person_id: str, info_type: str, person_name: str): + """提取单个信息类型 + + Args: + person_id: 用户ID + info_type: 信息类型 + person_name: 用户名 + """ + start_time = time.time() + person_info_manager = get_person_info_manager() + + # 首先检查 info_list 缓存 + info_list = await person_info_manager.get_value(person_id, "info_list") or [] + cached_info = None + + # 查找对应的 info_type + for info_item in info_list: + if info_item.get("info_type") == info_type: + cached_info = info_item.get("info_content") + logger.debug(f"{self.log_prefix} 在info_list中找到 {person_name} 的 {info_type} 信息: {cached_info}") + break + + # 如果缓存中有信息，直接使用 + if cached_info: + if person_id not in self.info_fetched_cache: + self.info_fetched_cache[person_id] = {} + + self.info_fetched_cache[person_id][info_type] = { + "info": cached_info, + "ttl": 2, + "start_time": start_time, + "person_name": person_name, + "unknow": cached_info == "none", + } + logger.info(f"{self.log_prefix} 记得 {person_name} 的 {info_type}: {cached_info}") + return + + # 如果缓存中没有，尝试从用户档案中提取 + try: + person_impression = await person_info_manager.get_value(person_id, "impression") + points = await person_info_manager.get_value(person_id, "points") + + # 构建印象信息块 + if person_impression: + person_impression_block = ( + f"<对{person_name}的总体了解>\n{person_impression}\n" + ) + else: + person_impression_block = "" + + # 构建要点信息块 + if points: + points_text = "\n".join([f"{point[2]}:{point[0]}" for point in points]) + points_text_block = f"<对{person_name}的近期了解>\n{points_text}\n" + else: + points_text_block = "" + + # 如果完全没有用户信息 + if not points_text_block and not person_impression_block: + if person_id not in self.info_fetched_cache: + self.info_fetched_cache[person_id] = {} + self.info_fetched_cache[person_id][info_type] = { + "info": "none", + "ttl": 2, + "start_time": start_time, + "person_name": person_name, + "unknow": True, + } + logger.info(f"{self.log_prefix} 完全不认识 {person_name}") + await self._save_info_to_cache(person_id, info_type, "none") + return + + # 使用LLM提取信息 + nickname_str = ",".join(global_config.bot.alias_names) + name_block = f"你的名字是{global_config.bot.nickname},你的昵称有{nickname_str}，有人也会用这些昵称称呼你。" + + prompt = (await global_prompt_manager.get_prompt_async("real_time_fetch_person_info_prompt")).format( + name_block=name_block, + info_type=info_type, + person_impression_block=person_impression_block, + person_name=person_name, + info_json_str=f'"{info_type}": "有关{info_type}的信息内容"', + points_text_block=points_text_block, + ) + + # 使用小模型进行即时提取 + content, _ = await self.instant_llm_model.generate_response_async(prompt=prompt) + + if content: + content_json = json.loads(repair_json(content)) + if info_type in content_json: + info_content = content_json[info_type] + is_unknown = info_content == "none" or not info_content + + # 保存到运行时缓存 + if person_id not in self.info_fetched_cache: + self.info_fetched_cache[person_id] = {} + self.info_fetched_cache[person_id][info_type] = { + "info": "unknow" if is_unknown else info_content, + "ttl": 3, + "start_time": start_time, + "person_name": person_name, + "unknow": is_unknown, + } + + # 保存到持久化缓存 (info_list) + await self._save_info_to_cache(person_id, info_type, info_content if not is_unknown else "none") + + if not is_unknown: + logger.info(f"{self.log_prefix} 思考得到，{person_name} 的 {info_type}: {info_content}") + else: + logger.info(f"{self.log_prefix} 思考了也不知道{person_name} 的 {info_type} 信息") + else: + logger.warning(f"{self.log_prefix} 小模型返回空结果，获取 {person_name} 的 {info_type} 信息失败。") + + except Exception as e: + logger.error(f"{self.log_prefix} 执行信息提取时出错: {e}") + logger.error(traceback.format_exc()) + + def _organize_known_info(self) -> str: + """组织已知的用户信息为字符串 + + Returns: + str: 格式化的用户信息字符串 + """ + persons_infos_str = "" + + if self.info_fetched_cache: + persons_with_known_info = [] # 有已知信息的人员 + persons_with_unknown_info = [] # 有未知信息的人员 + + for person_id in self.info_fetched_cache: + person_known_infos = [] + person_unknown_infos = [] + person_name = "" + + for info_type in self.info_fetched_cache[person_id]: + person_name = self.info_fetched_cache[person_id][info_type]["person_name"] + if not self.info_fetched_cache[person_id][info_type]["unknow"]: + info_content = self.info_fetched_cache[person_id][info_type]["info"] + person_known_infos.append(f"[{info_type}]：{info_content}") + else: + person_unknown_infos.append(info_type) + + # 如果有已知信息，添加到已知信息列表 + if person_known_infos: + known_info_str = "；".join(person_known_infos) + "；" + persons_with_known_info.append((person_name, known_info_str)) + + # 如果有未知信息，添加到未知信息列表 + if person_unknown_infos: + persons_with_unknown_info.append((person_name, person_unknown_infos)) + + # 先输出有已知信息的人员 + for person_name, known_info_str in persons_with_known_info: + persons_infos_str += f"你对 {person_name} 的了解：{known_info_str}\n" + + # 统一处理未知信息，避免重复的警告文本 + if persons_with_unknown_info: + unknown_persons_details = [] + for person_name, unknown_types in persons_with_unknown_info: + unknown_types_str = "、".join(unknown_types) + unknown_persons_details.append(f"{person_name}的[{unknown_types_str}]") + + if len(unknown_persons_details) == 1: + persons_infos_str += ( + f"你不了解{unknown_persons_details[0]}信息，不要胡乱回答，可以直接说不知道或忘记了；\n" + ) + else: + unknown_all_str = "、".join(unknown_persons_details) + persons_infos_str += f"你不了解{unknown_all_str}等信息，不要胡乱回答，可以直接说不知道或忘记了；\n" + + return persons_infos_str + + async def _save_info_to_cache(self, person_id: str, info_type: str, info_content: str): + """将提取到的信息保存到 person_info 的 info_list 字段中 + + Args: + person_id: 用户ID + info_type: 信息类型 + info_content: 信息内容 + """ + try: + person_info_manager = get_person_info_manager() + + # 获取现有的 info_list + info_list = await person_info_manager.get_value(person_id, "info_list") or [] + + # 查找是否已存在相同 info_type 的记录 + found_index = -1 + for i, info_item in enumerate(info_list): + if isinstance(info_item, dict) and info_item.get("info_type") == info_type: + found_index = i + break + + # 创建新的信息记录 + new_info_item = { + "info_type": info_type, + "info_content": info_content, + } + + if found_index >= 0: + # 更新现有记录 + info_list[found_index] = new_info_item + logger.info(f"{self.log_prefix} [缓存更新] 更新 {person_id} 的 {info_type} 信息缓存") + else: + # 添加新记录 + info_list.append(new_info_item) + logger.info(f"{self.log_prefix} [缓存保存] 新增 {person_id} 的 {info_type} 信息缓存") + + # 保存更新后的 info_list + await person_info_manager.update_one_field(person_id, "info_list", info_list) + + except Exception as e: + logger.error(f"{self.log_prefix} [缓存保存] 保存信息到缓存失败: {e}") + logger.error(traceback.format_exc()) + + +class RelationshipFetcherManager: + """关系提取器管理器 + + 管理不同 chat_id 的 RelationshipFetcher 实例 + """ + + def __init__(self): + self._fetchers: Dict[str, RelationshipFetcher] = {} + + def get_fetcher(self, chat_id: str) -> RelationshipFetcher: + """获取或创建指定 chat_id 的 RelationshipFetcher + + Args: + chat_id: 聊天ID + + Returns: + RelationshipFetcher: 关系提取器实例 + """ + if chat_id not in self._fetchers: + self._fetchers[chat_id] = RelationshipFetcher(chat_id) + return self._fetchers[chat_id] + + def remove_fetcher(self, chat_id: str): + """移除指定 chat_id 的 RelationshipFetcher + + Args: + chat_id: 聊天ID + """ + if chat_id in self._fetchers: + del self._fetchers[chat_id] + + def clear_all(self): + """清空所有 RelationshipFetcher""" + self._fetchers.clear() + + def get_active_chat_ids(self) -> List[str]: + """获取所有活跃的 chat_id 列表""" + return list(self._fetchers.keys()) + + +# 全局管理器实例 +relationship_fetcher_manager = RelationshipFetcherManager() + + +init_real_time_info_prompts() diff --git a/src/plugin_system/apis/generator_api.py b/src/plugin_system/apis/generator_api.py index 8130d9b4f..9f7f136be 100644 --- a/src/plugin_system/apis/generator_api.py +++ b/src/plugin_system/apis/generator_api.py @@ -8,10 +8,13 @@ success, reply_set = await generator_api.generate_reply(chat_stream, action_data, reasoning) """ -from typing import Tuple, Any, Dict, List +import traceback +from typing import Tuple, Any, Dict, List, Optional from src.common.logger import get_logger from src.chat.replyer.default_generator import DefaultReplyer -from src.chat.message_receive.chat_stream import get_chat_manager +from src.chat.message_receive.chat_stream import ChatStream +from src.chat.utils.utils import process_llm_response +from src.chat.replyer.replyer_manager import replyer_manager logger = get_logger("generator_api") @@ -21,46 +24,39 @@ logger = get_logger("generator_api") # ============================================================================= -def get_replyer(chat_stream=None, chat_id: str = None) -> DefaultReplyer: +def get_replyer( + chat_stream: Optional[ChatStream] = None, + chat_id: Optional[str] = None, + enable_tool: bool = False, + model_configs: Optional[List[Dict[str, Any]]] = None, + request_type: str = "replyer", +) -> Optional[DefaultReplyer]: """获取回复器对象 - 优先使用chat_stream，如果没有则使用chat_id直接查找 + 优先使用chat_stream，如果没有则使用chat_id直接查找。 + 使用 ReplyerManager 来管理实例，避免重复创建。 Args: chat_stream: 聊天流对象（优先） chat_id: 聊天ID（实际上就是stream_id） + model_configs: 模型配置列表 + request_type: 请求类型 Returns: - Optional[Any]: 回复器对象，如果获取失败则返回None + Optional[DefaultReplyer]: 回复器对象，如果获取失败则返回None """ try: - # 优先使用聊天流 - if chat_stream: - logger.debug("[GeneratorAPI] 使用聊天流获取回复器") - return DefaultReplyer(chat_stream=chat_stream) - - # 使用chat_id直接查找（chat_id即为stream_id） - if chat_id: - logger.debug("[GeneratorAPI] 使用chat_id获取回复器") - chat_manager = get_chat_manager() - if not chat_manager: - logger.warning("[GeneratorAPI] 无法获取聊天管理器") - return None - - # 直接使用chat_id作为stream_id查找 - target_stream = chat_manager.get_stream(chat_id) - - if target_stream is None: - logger.warning(f"[GeneratorAPI] 未找到匹配的聊天流 chat_id={chat_id}") - return None - - return DefaultReplyer(chat_stream=target_stream) - - logger.warning("[GeneratorAPI] 缺少必要参数，无法获取回复器") - return None - + logger.debug(f"[GeneratorAPI] 正在获取回复器，chat_id: {chat_id}, chat_stream: {'有' if chat_stream else '无'}") + return replyer_manager.get_replyer( + chat_stream=chat_stream, + chat_id=chat_id, + model_configs=model_configs, + request_type=request_type, + enable_tool=enable_tool, + ) except Exception as e: - logger.error(f"[GeneratorAPI] 获取回复器失败: {e}") + logger.error(f"[GeneratorAPI] 获取回复器时发生意外错误: {e}", exc_info=True) + traceback.print_exc() return None @@ -71,8 +67,18 @@ def get_replyer(chat_stream=None, chat_id: str = None) -> DefaultReplyer: async def generate_reply( chat_stream=None, - action_data: Dict[str, Any] = None, chat_id: str = None, + action_data: Dict[str, Any] = None, + reply_to: str = "", + relation_info: str = "", + extra_info: str = "", + available_actions: List[str] = None, + enable_tool: bool = False, + enable_splitter: bool = True, + enable_chinese_typo: bool = True, + return_prompt: bool = False, + model_configs: Optional[List[Dict[str, Any]]] = None, + request_type: str = "", ) -> Tuple[bool, List[Tuple[str, Any]]]: """生成回复 @@ -80,13 +86,17 @@ async def generate_reply( chat_stream: 聊天流对象（优先） action_data: 动作数据 chat_id: 聊天ID（备用） - + enable_splitter: 是否启用消息分割器 + enable_chinese_typo: 是否启用错字生成器 + return_prompt: 是否返回提示词 Returns: Tuple[bool, List[Tuple[str, Any]]]: (是否成功, 回复集合) """ try: # 获取回复器 - replyer = get_replyer(chat_stream, chat_id) + replyer = get_replyer( + chat_stream, chat_id, model_configs=model_configs, request_type=request_type, enable_tool=enable_tool + ) if not replyer: logger.error("[GeneratorAPI] 无法获取回复器") return False, [] @@ -94,16 +104,25 @@ async def generate_reply( logger.info("[GeneratorAPI] 开始生成回复") # 调用回复器生成回复 - success, reply_set = await replyer.generate_reply_with_context( + success, content, prompt = await replyer.generate_reply_with_context( reply_data=action_data or {}, + reply_to=reply_to, + relation_info=relation_info, + extra_info=extra_info, + available_actions=available_actions, ) + reply_set = await process_human_text(content, enable_splitter, enable_chinese_typo) + if success: logger.info(f"[GeneratorAPI] 回复生成成功，生成了 {len(reply_set)} 个回复项") else: logger.warning("[GeneratorAPI] 回复生成失败") - return success, reply_set or [] + if return_prompt: + return success, reply_set or [], prompt + else: + return success, reply_set or [] except Exception as e: logger.error(f"[GeneratorAPI] 生成回复时出错: {e}") @@ -114,6 +133,9 @@ async def rewrite_reply( chat_stream=None, reply_data: Dict[str, Any] = None, chat_id: str = None, + enable_splitter: bool = True, + enable_chinese_typo: bool = True, + model_configs: Optional[List[Dict[str, Any]]] = None, ) -> Tuple[bool, List[Tuple[str, Any]]]: """重写回复 @@ -121,13 +143,15 @@ async def rewrite_reply( chat_stream: 聊天流对象（优先） reply_data: 回复数据 chat_id: 聊天ID（备用） + enable_splitter: 是否启用消息分割器 + enable_chinese_typo: 是否启用错字生成器 Returns: Tuple[bool, List[Tuple[str, Any]]]: (是否成功, 回复集合) """ try: # 获取回复器 - replyer = get_replyer(chat_stream, chat_id) + replyer = get_replyer(chat_stream, chat_id, model_configs=model_configs) if not replyer: logger.error("[GeneratorAPI] 无法获取回复器") return False, [] @@ -135,9 +159,9 @@ async def rewrite_reply( logger.info("[GeneratorAPI] 开始重写回复") # 调用回复器重写回复 - success, reply_set = await replyer.rewrite_reply_with_context( - reply_data=reply_data or {}, - ) + success, content = await replyer.rewrite_reply_with_context(reply_data=reply_data or {}) + + reply_set = await process_human_text(content, enable_splitter, enable_chinese_typo) if success: logger.info(f"[GeneratorAPI] 重写回复成功，生成了 {len(reply_set)} 个回复项") @@ -149,3 +173,26 @@ async def rewrite_reply( except Exception as e: logger.error(f"[GeneratorAPI] 重写回复时出错: {e}") return False, [] + + +async def process_human_text(content: str, enable_splitter: bool, enable_chinese_typo: bool) -> List[Tuple[str, Any]]: + """将文本处理为更拟人化的文本 + + Args: + content: 文本内容 + enable_splitter: 是否启用消息分割器 + enable_chinese_typo: 是否启用错字生成器 + """ + try: + processed_response = process_llm_response(content, enable_splitter, enable_chinese_typo) + + reply_set = [] + for str in processed_response: + reply_seg = ("text", str) + reply_set.append(reply_seg) + + return reply_set + + except Exception as e: + logger.error(f"[GeneratorAPI] 处理人形文本时出错: {e}") + return [] diff --git a/src/plugin_system/apis/send_api.py b/src/plugin_system/apis/send_api.py index fdf793f14..645f2b4dc 100644 --- a/src/plugin_system/apis/send_api.py +++ b/src/plugin_system/apis/send_api.py @@ -22,6 +22,7 @@ import traceback import time import difflib +import re from typing import Optional, Union from src.common.logger import get_logger @@ -171,7 +172,41 @@ async def _find_reply_message(target_stream, reply_to: str) -> Optional[MessageR person_id = get_person_info_manager().get_person_id(platform, user_id) person_name = await get_person_info_manager().get_value(person_id, "person_name") if person_name == sender: - similarity = difflib.SequenceMatcher(None, text, message["processed_plain_text"]).ratio() + translate_text = message["processed_plain_text"] + + # 检查是否有 回复 字段 + reply_pattern = r"回复<([^:<>]+):([^:<>]+)>" + match = re.search(reply_pattern, translate_text) + if match: + aaa = match.group(1) + bbb = match.group(2) + reply_person_id = get_person_info_manager().get_person_id(platform, bbb) + reply_person_name = await get_person_info_manager().get_value(reply_person_id, "person_name") + if not reply_person_name: + reply_person_name = aaa + # 在内容前加上回复信息 + translate_text = re.sub(reply_pattern, f"回复 {reply_person_name}", translate_text, count=1) + + # 检查是否有 @ 字段 + at_pattern = r"@<([^:<>]+):([^:<>]+)>" + at_matches = list(re.finditer(at_pattern, translate_text)) + if at_matches: + new_content = "" + last_end = 0 + for m in at_matches: + new_content += translate_text[last_end : m.start()] + aaa = m.group(1) + bbb = m.group(2) + at_person_id = get_person_info_manager().get_person_id(platform, bbb) + at_person_name = await get_person_info_manager().get_value(at_person_id, "person_name") + if not at_person_name: + at_person_name = aaa + new_content += f"@{at_person_name}" + last_end = m.end() + new_content += translate_text[last_end:] + translate_text = new_content + + similarity = difflib.SequenceMatcher(None, text, translate_text).ratio() if similarity >= 0.9: find_msg = message break diff --git a/src/plugin_system/utils/manifest_utils.py b/src/plugin_system/utils/manifest_utils.py index 7db2321ae..7be7ba900 100644 --- a/src/plugin_system/utils/manifest_utils.py +++ b/src/plugin_system/utils/manifest_utils.py @@ -17,9 +17,27 @@ logger = get_logger("manifest_utils") class VersionComparator: """版本号比较器 - 支持语义化版本号比较，自动处理snapshot版本 + 支持语义化版本号比较，自动处理snapshot版本，并支持向前兼容性检查 """ + # 版本兼容性映射表（硬编码） + # 格式: {插件最大支持版本: [实际兼容的版本列表]} + COMPATIBILITY_MAP = { + # 0.8.x 系列向前兼容规则 + "0.8.0": ["0.8.1", "0.8.2", "0.8.3", "0.8.4", "0.8.5", "0.8.6", "0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.1": ["0.8.2", "0.8.3", "0.8.4", "0.8.5", "0.8.6", "0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.2": ["0.8.3", "0.8.4", "0.8.5", "0.8.6", "0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.3": ["0.8.4", "0.8.5", "0.8.6", "0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.4": ["0.8.5", "0.8.6", "0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.5": ["0.8.6", "0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.6": ["0.8.7", "0.8.8", "0.8.9", "0.8.10"], + "0.8.7": ["0.8.8", "0.8.9", "0.8.10"], + "0.8.8": ["0.8.9", "0.8.10"], + "0.8.9": ["0.8.10"], + # 可以根据需要添加更多兼容映射 + # "0.9.0": ["0.9.1", "0.9.2", "0.9.3"], # 示例：0.9.x系列兼容 + } + @staticmethod def normalize_version(version: str) -> str: """标准化版本号，移除snapshot标识 @@ -88,9 +106,31 @@ class VersionComparator: else: return 0 + @staticmethod + def check_forward_compatibility(current_version: str, max_version: str) -> Tuple[bool, str]: + """检查向前兼容性（仅使用兼容性映射表） + + Args: + current_version: 当前版本 + max_version: 插件声明的最大支持版本 + + Returns: + Tuple[bool, str]: (是否兼容, 兼容信息) + """ + current_normalized = VersionComparator.normalize_version(current_version) + max_normalized = VersionComparator.normalize_version(max_version) + + # 检查兼容性映射表 + if max_normalized in VersionComparator.COMPATIBILITY_MAP: + compatible_versions = VersionComparator.COMPATIBILITY_MAP[max_normalized] + if current_normalized in compatible_versions: + return True, f"根据兼容性映射表，版本 {current_normalized} 与 {max_normalized} 兼容" + + return False, "" + @staticmethod def is_version_in_range(version: str, min_version: str = "", max_version: str = "") -> Tuple[bool, str]: - """检查版本是否在指定范围内 + """检查版本是否在指定范围内，支持兼容性检查 Args: version: 要检查的版本号 @@ -98,7 +138,7 @@ class VersionComparator: max_version: 最大版本号（可选） Returns: - Tuple[bool, str]: (是否兼容, 错误信息) + Tuple[bool, str]: (是否兼容, 错误信息或兼容信息) """ if not min_version and not max_version: return True, "" @@ -114,8 +154,19 @@ class VersionComparator: # 检查最大版本 if max_version: max_normalized = VersionComparator.normalize_version(max_version) - if VersionComparator.compare_versions(version_normalized, max_normalized) > 0: - return False, f"版本 {version_normalized} 高于最大支持版本 {max_normalized}" + comparison = VersionComparator.compare_versions(version_normalized, max_normalized) + + if comparison > 0: + # 严格版本检查失败，尝试兼容性检查 + is_compatible, compat_msg = VersionComparator.check_forward_compatibility( + version_normalized, max_normalized + ) + + if is_compatible: + logger.info(f"版本兼容性检查：{compat_msg}") + return True, compat_msg + else: + return False, f"版本 {version_normalized} 高于最大支持版本 {max_normalized}，且无兼容性映射" return True, "" @@ -128,6 +179,29 @@ class VersionComparator: """ return VersionComparator.normalize_version(MMC_VERSION) + @staticmethod + def add_compatibility_mapping(base_version: str, compatible_versions: list) -> None: + """动态添加兼容性映射 + + Args: + base_version: 基础版本（插件声明的最大支持版本） + compatible_versions: 兼容的版本列表 + """ + base_normalized = VersionComparator.normalize_version(base_version) + VersionComparator.COMPATIBILITY_MAP[base_normalized] = [ + VersionComparator.normalize_version(v) for v in compatible_versions + ] + logger.info(f"添加兼容性映射：{base_normalized} -> {compatible_versions}") + + @staticmethod + def get_compatibility_info() -> Dict[str, list]: + """获取当前的兼容性映射表 + + Returns: + Dict[str, list]: 兼容性映射表的副本 + """ + return VersionComparator.COMPATIBILITY_MAP.copy() + class ManifestValidator: """Manifest文件验证器""" diff --git a/src/plugins/built_in/core_actions/_manifest.json b/src/plugins/built_in/core_actions/_manifest.json index 1d1266f67..ba1b20d6b 100644 --- a/src/plugins/built_in/core_actions/_manifest.json +++ b/src/plugins/built_in/core_actions/_manifest.json @@ -10,8 +10,7 @@ "license": "GPL-v3.0-or-later", "host_application": { - "min_version": "0.8.0", - "max_version": "0.8.0" + "min_version": "0.8.0" }, "homepage_url": "https://github.com/MaiM-with-u/maibot", "repository_url": "https://github.com/MaiM-with-u/maibot", diff --git a/src/plugins/built_in/core_actions/emoji.py b/src/plugins/built_in/core_actions/emoji.py new file mode 100644 index 000000000..c1fe0f0fb --- /dev/null +++ b/src/plugins/built_in/core_actions/emoji.py @@ -0,0 +1,84 @@ +from typing import Tuple + +# 导入新插件系统 +from src.plugin_system import BaseAction, ActionActivationType, ChatMode + +# 导入依赖的系统组件 +from src.common.logger import get_logger + +# 导入API模块 - 标准Python包方式 +from src.plugin_system.apis import emoji_api +from src.plugins.built_in.core_actions.no_reply import NoReplyAction + + +logger = get_logger("core_actions") + + +class EmojiAction(BaseAction): + """表情动作 - 发送表情包""" + + # 激活设置 + focus_activation_type = ActionActivationType.LLM_JUDGE + normal_activation_type = ActionActivationType.RANDOM + mode_enable = ChatMode.ALL + parallel_action = True + random_activation_probability = 0.2 # 默认值，可通过配置覆盖 + + # 动作基本信息 + action_name = "emoji" + action_description = "发送表情包辅助表达情绪" + + # LLM判断提示词 + llm_judge_prompt = """ + 判定是否需要使用表情动作的条件： + 1. 用户明确要求使用表情包 + 2. 这是一个适合表达强烈情绪的场合 + 3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否" + + 请回答"是"或"否"。 + """ + + # 动作参数定义 + action_parameters = {"description": "文字描述你想要发送的表情包内容"} + + # 动作使用场景 + action_require = [ + "发送表情包辅助表达情绪", + "表达情绪时可以选择使用", + "不要连续发送，如果你已经发过[表情包]，就不要选择此动作", + ] + + # 关联类型 + associated_types = ["emoji"] + + async def execute(self) -> Tuple[bool, str]: + """执行表情动作""" + logger.info(f"{self.log_prefix} 决定发送表情") + + try: + # 1. 根据描述选择表情包 + description = self.action_data.get("description", "") + emoji_result = await emoji_api.get_by_description(description) + + if not emoji_result: + logger.warning(f"{self.log_prefix} 未找到匹配描述 '{description}' 的表情包") + return False, f"未找到匹配 '{description}' 的表情包" + + emoji_base64, emoji_description, matched_emotion = emoji_result + logger.info(f"{self.log_prefix} 找到表情包: {emoji_description}, 匹配情感: {matched_emotion}") + + # 使用BaseAction的便捷方法发送表情包 + success = await self.send_emoji(emoji_base64) + + if not success: + logger.error(f"{self.log_prefix} 表情包发送失败") + return False, "表情包发送失败" + + # 重置NoReplyAction的连续计数器 + NoReplyAction.reset_consecutive_count() + + return True, f"发送表情包: {emoji_description}" + + except Exception as e: + logger.error(f"{self.log_prefix} 表情动作执行失败: {e}") + return False, f"表情发送失败: {str(e)}" diff --git a/src/plugins/built_in/core_actions/plugin.py b/src/plugins/built_in/core_actions/plugin.py index dcd4ce5cf..cb469ae87 100644 --- a/src/plugins/built_in/core_actions/plugin.py +++ b/src/plugins/built_in/core_actions/plugin.py @@ -12,13 +12,15 @@ from typing import List, Tuple, Type # 导入新插件系统 from src.plugin_system import BasePlugin, register_plugin, BaseAction, ComponentInfo, ActionActivationType, ChatMode from src.plugin_system.base.config_types import ConfigField +from src.config.config import global_config # 导入依赖的系统组件 from src.common.logger import get_logger # 导入API模块 - 标准Python包方式 -from src.plugin_system.apis import emoji_api, generator_api, message_api +from src.plugin_system.apis import generator_api, message_api from src.plugins.built_in.core_actions.no_reply import NoReplyAction +from src.plugins.built_in.core_actions.emoji import EmojiAction logger = get_logger("core_actions") @@ -61,6 +63,8 @@ class ReplyAction(BaseAction): success, reply_set = await generator_api.generate_reply( action_data=self.action_data, chat_id=self.chat_id, + request_type="focus.replyer", + enable_tool=global_config.tool.enable_in_focus_chat, ) # 检查从start_time以来的新消息数量 @@ -109,72 +113,6 @@ class ReplyAction(BaseAction): return False, f"回复失败: {str(e)}" -class EmojiAction(BaseAction): - """表情动作 - 发送表情包""" - - # 激活设置 - focus_activation_type = ActionActivationType.LLM_JUDGE - normal_activation_type = ActionActivationType.RANDOM - mode_enable = ChatMode.ALL - parallel_action = True - random_activation_probability = 0.2 # 默认值，可通过配置覆盖 - - # 动作基本信息 - action_name = "emoji" - action_description = "发送表情包辅助表达情绪" - - # LLM判断提示词 - llm_judge_prompt = """ - 判定是否需要使用表情动作的条件： - 1. 用户明确要求使用表情包 - 2. 这是一个适合表达强烈情绪的场合 - 3. 不要发送太多表情包，如果你已经发送过多个表情包则回答"否" - - 请回答"是"或"否"。 - """ - - # 动作参数定义 - action_parameters = {"description": "文字描述你想要发送的表情包内容"} - - # 动作使用场景 - action_require = ["表达情绪时可以选择使用", "重点：不要连续发，如果你已经发过[表情包]，就不要选择此动作"] - - # 关联类型 - associated_types = ["emoji"] - - async def execute(self) -> Tuple[bool, str]: - """执行表情动作""" - logger.info(f"{self.log_prefix} 决定发送表情") - - try: - # 1. 根据描述选择表情包 - description = self.action_data.get("description", "") - emoji_result = await emoji_api.get_by_description(description) - - if not emoji_result: - logger.warning(f"{self.log_prefix} 未找到匹配描述 '{description}' 的表情包") - return False, f"未找到匹配 '{description}' 的表情包" - - emoji_base64, emoji_description, matched_emotion = emoji_result - logger.info(f"{self.log_prefix} 找到表情包: {emoji_description}, 匹配情感: {matched_emotion}") - - # 使用BaseAction的便捷方法发送表情包 - success = await self.send_emoji(emoji_base64) - - if not success: - logger.error(f"{self.log_prefix} 表情包发送失败") - return False, "表情包发送失败" - - # 重置NoReplyAction的连续计数器 - NoReplyAction.reset_consecutive_count() - - return True, f"发送表情包: {emoji_description}" - - except Exception as e: - logger.error(f"{self.log_prefix} 表情动作执行失败: {e}") - return False, f"表情发送失败: {str(e)}" - - @register_plugin class CoreActionsPlugin(BasePlugin): """核心动作插件 @@ -197,21 +135,18 @@ class CoreActionsPlugin(BasePlugin): "plugin": "插件启用配置", "components": "核心组件启用配置", "no_reply": "不回复动作配置（智能等待机制）", - "emoji": "表情动作配置", } # 配置Schema定义 config_schema = { "plugin": { "enabled": ConfigField(type=bool, default=True, description="是否启用插件"), - "config_version": ConfigField(type=str, default="0.1.0", description="配置文件版本"), + "config_version": ConfigField(type=str, default="0.3.1", description="配置文件版本"), }, "components": { "enable_reply": ConfigField(type=bool, default=True, description="是否启用'回复'动作"), "enable_no_reply": ConfigField(type=bool, default=True, description="是否启用'不回复'动作"), "enable_emoji": ConfigField(type=bool, default=True, description="是否启用'表情'动作"), - "enable_change_to_focus": ConfigField(type=bool, default=True, description="是否启用'切换到专注模式'动作"), - "enable_exit_focus": ConfigField(type=bool, default=True, description="是否启用'退出专注模式'动作"), }, "no_reply": { "max_timeout": ConfigField(type=int, default=1200, description="最大等待超时时间（秒）"), @@ -231,18 +166,13 @@ class CoreActionsPlugin(BasePlugin): type=int, default=600, description="回复频率检查窗口时间（秒）", example=600 ), }, - "emoji": { - "random_probability": ConfigField( - type=float, default=0.1, description="Normal模式下，随机发送表情的概率（0.0到1.0）", example=0.15 - ) - }, } def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]: """返回插件包含的组件列表""" # --- 从配置动态设置Action/Command --- - emoji_chance = self.get_config("emoji.random_probability", 0.1) + emoji_chance = global_config.normal_chat.emoji_chance EmojiAction.random_activation_probability = emoji_chance no_reply_probability = self.get_config("no_reply.random_probability", 0.8) diff --git a/src/plugins/built_in/doubao_pic_plugin/_manifest.json b/src/plugins/built_in/doubao_pic_plugin/_manifest.json deleted file mode 100644 index 92912c400..000000000 --- a/src/plugins/built_in/doubao_pic_plugin/_manifest.json +++ /dev/null @@ -1,45 +0,0 @@ -{ - "manifest_version": 1, - "name": "豆包图片生成插件 (Doubao Image Generator)", - "version": "2.0.0", - "description": "基于火山引擎豆包模型的AI图片生成插件，支持智能LLM判定、高质量图片生成、结果缓存和多尺寸支持。", - "author": { - "name": "MaiBot团队", - "url": "https://github.com/MaiM-with-u" - }, - "license": "GPL-v3.0-or-later", - - "host_application": { - "min_version": "0.8.0", - "max_version": "0.8.0" - }, - "homepage_url": "https://github.com/MaiM-with-u/maibot", - "repository_url": "https://github.com/MaiM-with-u/maibot", - "keywords": ["ai", "image", "generation", "doubao", "volcengine", "art"], - "categories": ["AI Tools", "Image Processing", "Content Generation"], - - "default_locale": "zh-CN", - "locales_path": "_locales", - - "plugin_info": { - "is_built_in": true, - "plugin_type": "content_generator", - "api_dependencies": ["volcengine"], - "components": [ - { - "type": "action", - "name": "doubao_image_generation", - "description": "根据描述使用火山引擎豆包API生成高质量图片", - "activation_modes": ["llm_judge", "keyword"], - "keywords": ["画", "图片", "生成", "画画", "绘制"] - } - ], - "features": [ - "智能LLM判定生成时机", - "高质量AI图片生成", - "结果缓存机制", - "多种图片尺寸支持", - "完整的错误处理" - ] - } -} diff --git a/src/plugins/built_in/doubao_pic_plugin/plugin.py b/src/plugins/built_in/doubao_pic_plugin/plugin.py deleted file mode 100644 index 28d37e88f..000000000 --- a/src/plugins/built_in/doubao_pic_plugin/plugin.py +++ /dev/null @@ -1,477 +0,0 @@ -""" -豆包图片生成插件 - -基于火山引擎豆包模型的AI图片生成插件。 - -功能特性： -- 智能LLM判定：根据聊天内容智能判断是否需要生成图片 -- 高质量图片生成：使用豆包Seed Dream模型生成图片 -- 结果缓存：避免重复生成相同内容的图片 -- 配置验证：自动验证和修复配置文件 -- 参数验证：完整的输入参数验证和错误处理 -- 多尺寸支持：支持多种图片尺寸生成 - -包含组件： -- 图片生成Action - 根据描述使用火山引擎API生成图片 -""" - -import asyncio -import json -import urllib.request -import urllib.error -import base64 -import traceback -from typing import List, Tuple, Type, Optional - -# 导入新插件系统 -from src.plugin_system.base.base_plugin import BasePlugin -from src.plugin_system.base.base_plugin import register_plugin -from src.plugin_system.base.base_action import BaseAction -from src.plugin_system.base.component_types import ComponentInfo, ActionActivationType, ChatMode -from src.plugin_system.base.config_types import ConfigField -from src.common.logger import get_logger - -logger = get_logger("doubao_pic_plugin") - - -# ===== Action组件 ===== - - -class DoubaoImageGenerationAction(BaseAction): - """豆包图片生成Action - 根据描述使用火山引擎API生成图片""" - - # 激活设置 - focus_activation_type = ActionActivationType.LLM_JUDGE # Focus模式使用LLM判定，精确理解需求 - normal_activation_type = ActionActivationType.KEYWORD # Normal模式使用关键词激活，快速响应 - mode_enable = ChatMode.ALL - parallel_action = True - - # 动作基本信息 - action_name = "doubao_image_generation" - action_description = ( - "可以根据特定的描述，生成并发送一张图片，如果没提供描述，就根据聊天内容生成,你可以立刻画好，不用等待" - ) - - # 关键词设置（用于Normal模式） - activation_keywords = ["画", "绘制", "生成图片", "画图", "draw", "paint", "图片生成"] - keyword_case_sensitive = False - - # LLM判定提示词（用于Focus模式） - llm_judge_prompt = """ -判定是否需要使用图片生成动作的条件： -1. 用户明确要求画图、生成图片或创作图像 -2. 用户描述了想要看到的画面或场景 -3. 对话中提到需要视觉化展示某些概念 -4. 用户想要创意图片或艺术作品 - -适合使用的情况： -- "画一张..."、"画个..."、"生成图片" -- "我想看看...的样子" -- "能画出...吗" -- "创作一幅..." - -绝对不要使用的情况： -1. 纯文字聊天和问答 -2. 只是提到"图片"、"画"等词但不是要求生成 -3. 谈论已存在的图片或照片 -4. 技术讨论中提到绘图概念但无生成需求 -5. 用户明确表示不需要图片时 -""" - - # 动作参数定义 - action_parameters = { - "description": "图片描述，输入你想要生成并发送的图片的描述，必填", - "size": "图片尺寸，例如 '1024x1024' (可选, 默认从配置或 '1024x1024')", - } - - # 动作使用场景 - action_require = [ - "当有人让你画东西时使用，你可以立刻画好，不用等待", - "当有人要求你生成并发送一张图片时使用", - "当有人让你画一张图时使用", - ] - - # 关联类型 - associated_types = ["image", "text"] - - # 简单的请求缓存，避免短时间内重复请求 - _request_cache = {} - _cache_max_size = 10 - - async def execute(self) -> Tuple[bool, Optional[str]]: - """执行图片生成动作""" - logger.info(f"{self.log_prefix} 执行豆包图片生成动作") - - # 配置验证 - http_base_url = self.api.get_config("api.base_url") - http_api_key = self.api.get_config("api.volcano_generate_api_key") - - if not (http_base_url and http_api_key): - error_msg = "抱歉，图片生成功能所需的HTTP配置（如API地址或密钥）不完整，无法提供服务。" - await self.send_text(error_msg) - logger.error(f"{self.log_prefix} HTTP调用配置缺失: base_url 或 volcano_generate_api_key.") - return False, "HTTP配置不完整" - - # API密钥验证 - if http_api_key == "YOUR_DOUBAO_API_KEY_HERE": - error_msg = "图片生成功能尚未配置，请设置正确的API密钥。" - await self.send_text(error_msg) - logger.error(f"{self.log_prefix} API密钥未配置") - return False, "API密钥未配置" - - # 参数验证 - description = self.action_data.get("description") - if not description or not description.strip(): - logger.warning(f"{self.log_prefix} 图片描述为空，无法生成图片。") - await self.send_text("你需要告诉我想要画什么样的图片哦~ 比如说'画一只可爱的小猫'") - return False, "图片描述为空" - - # 清理和验证描述 - description = description.strip() - if len(description) > 1000: # 限制描述长度 - description = description[:1000] - logger.info(f"{self.log_prefix} 图片描述过长，已截断") - - # 获取配置 - default_model = self.api.get_config("generation.default_model", "doubao-seedream-3-0-t2i-250415") - image_size = self.action_data.get("size", self.api.get_config("generation.default_size", "1024x1024")) - - # 验证图片尺寸格式 - if not self._validate_image_size(image_size): - logger.warning(f"{self.log_prefix} 无效的图片尺寸: {image_size}，使用默认值") - image_size = "1024x1024" - - # 检查缓存 - cache_key = self._get_cache_key(description, default_model, image_size) - if cache_key in self._request_cache: - cached_result = self._request_cache[cache_key] - logger.info(f"{self.log_prefix} 使用缓存的图片结果") - await self.send_text("我之前画过类似的图片，用之前的结果~") - - # 直接发送缓存的结果 - send_success = await self._send_image(cached_result) - if send_success: - await self.send_text("图片已发送！") - return True, "图片已发送(缓存)" - else: - # 缓存失败，清除这个缓存项并继续正常流程 - del self._request_cache[cache_key] - - # 获取其他配置参数 - guidance_scale_val = self._get_guidance_scale() - seed_val = self._get_seed() - watermark_val = self._get_watermark() - - await self.send_text( - f"收到！正在为您生成关于 '{description}' 的图片，请稍候...（模型: {default_model}, 尺寸: {image_size}）" - ) - - try: - success, result = await asyncio.to_thread( - self._make_http_image_request, - prompt=description, - model=default_model, - size=image_size, - seed=seed_val, - guidance_scale=guidance_scale_val, - watermark=watermark_val, - ) - except Exception as e: - logger.error(f"{self.log_prefix} (HTTP) 异步请求执行失败: {e!r}", exc_info=True) - traceback.print_exc() - success = False - result = f"图片生成服务遇到意外问题: {str(e)[:100]}" - - if success: - image_url = result - # print(f"image_url: {image_url}") - # print(f"result: {result}") - logger.info(f"{self.log_prefix} 图片URL获取成功: {image_url[:70]}... 下载并编码.") - - try: - encode_success, encode_result = await asyncio.to_thread(self._download_and_encode_base64, image_url) - except Exception as e: - logger.error(f"{self.log_prefix} (B64) 异步下载/编码失败: {e!r}", exc_info=True) - traceback.print_exc() - encode_success = False - encode_result = f"图片下载或编码时发生内部错误: {str(e)[:100]}" - - if encode_success: - base64_image_string = encode_result - send_success = await self._send_image(base64_image_string) - if send_success: - # 缓存成功的结果 - self._request_cache[cache_key] = base64_image_string - self._cleanup_cache() - - await self.send_message_by_expressor("图片已发送！") - return True, "图片已成功生成并发送" - else: - print(f"send_success: {send_success}") - await self.send_message_by_expressor("图片已处理为Base64，但发送失败了。") - return False, "图片发送失败 (Base64)" - else: - await self.send_message_by_expressor(f"获取到图片URL，但在处理图片时失败了：{encode_result}") - return False, f"图片处理失败(Base64): {encode_result}" - else: - error_message = result - await self.send_message_by_expressor(f"哎呀，生成图片时遇到问题：{error_message}") - return False, f"图片生成失败: {error_message}" - - def _get_guidance_scale(self) -> float: - """获取guidance_scale配置值""" - guidance_scale_input = self.api.get_config("generation.default_guidance_scale", 2.5) - try: - return float(guidance_scale_input) - except (ValueError, TypeError): - logger.warning(f"{self.log_prefix} default_guidance_scale 值无效，使用默认值 2.5") - return 2.5 - - def _get_seed(self) -> int: - """获取seed配置值""" - seed_config_value = self.api.get_config("generation.default_seed") - if seed_config_value is not None: - try: - return int(seed_config_value) - except (ValueError, TypeError): - logger.warning(f"{self.log_prefix} default_seed 值无效，使用默认值 42") - return 42 - - def _get_watermark(self) -> bool: - """获取watermark配置值""" - watermark_source = self.api.get_config("generation.default_watermark", True) - if isinstance(watermark_source, bool): - return watermark_source - elif isinstance(watermark_source, str): - return watermark_source.lower() == "true" - else: - logger.warning(f"{self.log_prefix} default_watermark 值无效，使用默认值 True") - return True - - async def _send_image(self, base64_image: str) -> bool: - """发送图片""" - try: - # 使用聊天流信息确定发送目标 - chat_stream = self.api.get_service("chat_stream") - if not chat_stream: - logger.error(f"{self.log_prefix} 没有可用的聊天流发送图片") - return False - - if chat_stream.group_info: - # 群聊 - return await self.api.send_message_to_target( - message_type="image", - content=base64_image, - platform=chat_stream.platform, - target_id=str(chat_stream.group_info.group_id), - is_group=True, - display_message="发送生成的图片", - ) - else: - # 私聊 - return await self.api.send_message_to_target( - message_type="image", - content=base64_image, - platform=chat_stream.platform, - target_id=str(chat_stream.user_info.user_id), - is_group=False, - display_message="发送生成的图片", - ) - except Exception as e: - logger.error(f"{self.log_prefix} 发送图片时出错: {e}") - return False - - @classmethod - def _get_cache_key(cls, description: str, model: str, size: str) -> str: - """生成缓存键""" - return f"{description[:100]}|{model}|{size}" - - @classmethod - def _cleanup_cache(cls): - """清理缓存，保持大小在限制内""" - if len(cls._request_cache) > cls._cache_max_size: - keys_to_remove = list(cls._request_cache.keys())[: -cls._cache_max_size // 2] - for key in keys_to_remove: - del cls._request_cache[key] - - def _validate_image_size(self, image_size: str) -> bool: - """验证图片尺寸格式""" - try: - width, height = map(int, image_size.split("x")) - return 100 <= width <= 10000 and 100 <= height <= 10000 - except (ValueError, TypeError): - return False - - def _download_and_encode_base64(self, image_url: str) -> Tuple[bool, str]: - """下载图片并将其编码为Base64字符串""" - logger.info(f"{self.log_prefix} (B64) 下载并编码图片: {image_url[:70]}...") - try: - with urllib.request.urlopen(image_url, timeout=30) as response: - if response.status == 200: - image_bytes = response.read() - base64_encoded_image = base64.b64encode(image_bytes).decode("utf-8") - logger.info(f"{self.log_prefix} (B64) 图片下载编码完成. Base64长度: {len(base64_encoded_image)}") - return True, base64_encoded_image - else: - error_msg = f"下载图片失败 (状态: {response.status})" - logger.error(f"{self.log_prefix} (B64) {error_msg} URL: {image_url}") - return False, error_msg - except Exception as e: - logger.error(f"{self.log_prefix} (B64) 下载或编码时错误: {e!r}", exc_info=True) - traceback.print_exc() - return False, f"下载或编码图片时发生错误: {str(e)[:100]}" - - def _make_http_image_request( - self, prompt: str, model: str, size: str, seed: int, guidance_scale: float, watermark: bool - ) -> Tuple[bool, str]: - """发送HTTP请求生成图片""" - base_url = self.api.get_config("api.base_url") - generate_api_key = self.api.get_config("api.volcano_generate_api_key") - - endpoint = f"{base_url.rstrip('/')}/images/generations" - - payload_dict = { - "model": model, - "prompt": prompt, - "response_format": "url", - "size": size, - "guidance_scale": guidance_scale, - "watermark": watermark, - "seed": seed, - "api-key": generate_api_key, - } - - data = json.dumps(payload_dict).encode("utf-8") - headers = { - "Content-Type": "application/json", - "Accept": "application/json", - "Authorization": f"Bearer {generate_api_key}", - } - - logger.info(f"{self.log_prefix} (HTTP) 发起图片请求: {model}, Prompt: {prompt[:30]}... To: {endpoint}") - - req = urllib.request.Request(endpoint, data=data, headers=headers, method="POST") - - try: - with urllib.request.urlopen(req, timeout=60) as response: - response_status = response.status - response_body_bytes = response.read() - response_body_str = response_body_bytes.decode("utf-8") - - logger.info(f"{self.log_prefix} (HTTP) 响应: {response_status}. Preview: {response_body_str[:150]}...") - - if 200 <= response_status < 300: - response_data = json.loads(response_body_str) - image_url = None - if ( - isinstance(response_data.get("data"), list) - and response_data["data"] - and isinstance(response_data["data"][0], dict) - ): - image_url = response_data["data"][0].get("url") - elif response_data.get("url"): - image_url = response_data.get("url") - - if image_url: - logger.info(f"{self.log_prefix} (HTTP) 图片生成成功，URL: {image_url[:70]}...") - return True, image_url - else: - logger.error(f"{self.log_prefix} (HTTP) API成功但无图片URL") - return False, "图片生成API响应成功但未找到图片URL" - else: - logger.error(f"{self.log_prefix} (HTTP) API请求失败. 状态: {response.status}") - return False, f"图片API请求失败(状态码 {response.status})" - except Exception as e: - logger.error(f"{self.log_prefix} (HTTP) 图片生成时意外错误: {e!r}", exc_info=True) - traceback.print_exc() - return False, f"图片生成HTTP请求时发生意外错误: {str(e)[:100]}" - - -# ===== 插件主类 ===== - - -@register_plugin -class DoubaoImagePlugin(BasePlugin): - """豆包图片生成插件 - - 基于火山引擎豆包模型的AI图片生成插件： - - 图片生成Action：根据描述使用火山引擎API生成图片 - """ - - # 插件基本信息 - plugin_name = "doubao_pic_plugin" # 内部标识符 - enable_plugin = True - config_file_name = "config.toml" - - # 配置节描述 - config_section_descriptions = { - "plugin": "插件基本信息配置", - "api": "API相关配置，包含火山引擎API的访问信息", - "generation": "图片生成参数配置，控制生成图片的各种参数", - "cache": "结果缓存配置", - "components": "组件启用配置", - } - - # 配置Schema定义 - config_schema = { - "plugin": { - "name": ConfigField(type=str, default="doubao_pic_plugin", description="插件名称", required=True), - "version": ConfigField(type=str, default="2.0.0", description="插件版本号"), - "enabled": ConfigField(type=bool, default=False, description="是否启用插件"), - "description": ConfigField( - type=str, default="基于火山引擎豆包模型的AI图片生成插件", description="插件描述", required=True - ), - }, - "api": { - "base_url": ConfigField( - type=str, - default="https://ark.cn-beijing.volces.com/api/v3", - description="API基础URL", - example="https://api.example.com/v1", - ), - "volcano_generate_api_key": ConfigField( - type=str, default="YOUR_DOUBAO_API_KEY_HERE", description="火山引擎豆包API密钥", required=True - ), - }, - "generation": { - "default_model": ConfigField( - type=str, - default="doubao-seedream-3-0-t2i-250415", - description="默认使用的文生图模型", - choices=["doubao-seedream-3-0-t2i-250415", "doubao-seedream-2-0-t2i"], - ), - "default_size": ConfigField( - type=str, - default="1024x1024", - description="默认图片尺寸", - example="1024x1024", - choices=["1024x1024", "1024x1280", "1280x1024", "1024x1536", "1536x1024"], - ), - "default_watermark": ConfigField(type=bool, default=True, description="是否默认添加水印"), - "default_guidance_scale": ConfigField( - type=float, default=2.5, description="模型指导强度，影响图片与提示的关联性", example="2.0" - ), - "default_seed": ConfigField(type=int, default=42, description="随机种子，用于复现图片"), - }, - "cache": { - "enabled": ConfigField(type=bool, default=True, description="是否启用请求缓存"), - "max_size": ConfigField(type=int, default=10, description="最大缓存数量"), - }, - "components": { - "enable_image_generation": ConfigField(type=bool, default=True, description="是否启用图片生成Action") - }, - } - - def get_plugin_components(self) -> List[Tuple[ComponentInfo, Type]]: - """返回插件包含的组件列表""" - - # 从配置获取组件启用状态 - enable_image_generation = self.get_config("components.enable_image_generation", True) - - components = [] - - # 添加图片生成Action - if enable_image_generation: - components.append((DoubaoImageGenerationAction.get_action_info(), DoubaoImageGenerationAction)) - - return components diff --git a/src/plugins/built_in/mute_plugin/_manifest.json b/src/plugins/built_in/mute_plugin/_manifest.json index b8d919560..f990ba44e 100644 --- a/src/plugins/built_in/mute_plugin/_manifest.json +++ b/src/plugins/built_in/mute_plugin/_manifest.json @@ -10,7 +10,7 @@ "license": "GPL-v3.0-or-later", "host_application": { "min_version": "0.8.0", - "max_version": "0.8.0" + "max_version": "0.8.10" }, "keywords": ["mute", "ban", "moderation", "admin", "management", "group"], "categories": ["Moderation", "Group Management", "Admin Tools"], diff --git a/src/plugins/built_in/mute_plugin/plugin.py b/src/plugins/built_in/mute_plugin/plugin.py index 394d38f5d..43f5f81c4 100644 --- a/src/plugins/built_in/mute_plugin/plugin.py +++ b/src/plugins/built_in/mute_plugin/plugin.py @@ -369,10 +369,10 @@ class MuteCommand(BaseCommand): # 获取用户ID person_id = person_api.get_person_id_by_name(target) - user_id = person_api.get_person_value(person_id, "user_id") - if not user_id: - error_msg = f"未找到用户 {target} 的ID" - await self.send_text(f"❌ 找不到用户: {target}") + user_id = await person_api.get_person_value(person_id, "user_id") + if not user_id or user_id == "unknown": + error_msg = f"未找到用户 {target} 的ID，请输入person_name进行禁言" + await self.send_text(f"❌ 找不到用户 {target} 的ID，请输入person_name进行禁言，而不是qq号或者昵称") logger.error(f"{self.log_prefix} {error_msg}") return False, error_msg @@ -475,7 +475,9 @@ class MutePlugin(BasePlugin): }, "components": { "enable_smart_mute": ConfigField(type=bool, default=True, description="是否启用智能禁言Action"), - "enable_mute_command": ConfigField(type=bool, default=False, description="是否启用禁言命令Command"), + "enable_mute_command": ConfigField( + type=bool, default=False, description="是否启用禁言命令Command（调试用）" + ), }, "permissions": { "allowed_users": ConfigField( diff --git a/src/plugins/built_in/tts_plugin/_manifest.json b/src/plugins/built_in/tts_plugin/_manifest.json index be00637c1..be9f61b0a 100644 --- a/src/plugins/built_in/tts_plugin/_manifest.json +++ b/src/plugins/built_in/tts_plugin/_manifest.json @@ -11,7 +11,7 @@ "host_application": { "min_version": "0.8.0", - "max_version": "0.8.0" + "max_version": "0.8.10" }, "homepage_url": "https://github.com/MaiM-with-u/maibot", "repository_url": "https://github.com/MaiM-with-u/maibot", diff --git a/src/plugins/built_in/vtb_plugin/_manifest.json b/src/plugins/built_in/vtb_plugin/_manifest.json index 338c4a4d4..1cff37136 100644 --- a/src/plugins/built_in/vtb_plugin/_manifest.json +++ b/src/plugins/built_in/vtb_plugin/_manifest.json @@ -10,7 +10,7 @@ "license": "GPL-v3.0-or-later", "host_application": { "min_version": "0.8.0", - "max_version": "0.8.0" + "max_version": "0.8.10" }, "keywords": ["vtb", "vtuber", "emotion", "expression", "virtual", "streamer"], "categories": ["Entertainment", "Virtual Assistant", "Emotion"], diff --git a/src/tools/tool_can_use/get_knowledge.py b/src/tools/not_using/get_knowledge.py similarity index 100% rename from src/tools/tool_can_use/get_knowledge.py rename to src/tools/not_using/get_knowledge.py diff --git a/src/tools/tool_can_use/lpmm_get_knowledge.py b/src/tools/not_using/lpmm_get_knowledge.py similarity index 100% rename from src/tools/tool_can_use/lpmm_get_knowledge.py rename to src/tools/not_using/lpmm_get_knowledge.py diff --git a/src/tools/tool_can_use/rename_person_tool.py b/src/tools/tool_can_use/rename_person_tool.py index 71bdc0f76..0651e0c2c 100644 --- a/src/tools/tool_can_use/rename_person_tool.py +++ b/src/tools/tool_can_use/rename_person_tool.py @@ -1,4 +1,4 @@ -from src.tools.tool_can_use.base_tool import BaseTool, register_tool +from src.tools.tool_can_use.base_tool import BaseTool from src.person_info.person_info import get_person_info_manager from src.common.logger import get_logger import time @@ -102,7 +102,3 @@ class RenamePersonTool(BaseTool): error_msg = f"重命名失败: {str(e)}" logger.error(error_msg, exc_info=True) return {"type": "info_error", "id": f"rename_error_{time.time()}", "content": error_msg} - - -# 注册工具 -register_tool(RenamePersonTool) diff --git a/src/tools/tool_executor.py b/src/tools/tool_executor.py new file mode 100644 index 000000000..0673068cf --- /dev/null +++ b/src/tools/tool_executor.py @@ -0,0 +1,404 @@ +from src.llm_models.utils_model import LLMRequest +from src.config.config import global_config +import time +from src.common.logger import get_logger +from src.chat.utils.prompt_builder import Prompt, global_prompt_manager +from src.tools.tool_use import ToolUser +from src.chat.utils.json_utils import process_llm_tool_calls +from typing import List, Dict, Tuple, Optional + +logger = get_logger("tool_executor") + + +def init_tool_executor_prompt(): + """初始化工具执行器的提示词""" + tool_executor_prompt = """ +你是一个专门执行工具的助手。你的名字是{bot_name}。现在是{time_now}。 +群里正在进行的聊天内容： +{chat_history} + +现在，{sender}发送了内容:{target_message},你想要回复ta。 +请仔细分析聊天内容，考虑以下几点： +1. 内容中是否包含需要查询信息的问题 +2. 是否有明确的工具使用指令 + +If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed". +""" + Prompt(tool_executor_prompt, "tool_executor_prompt") + + +class ToolExecutor: + """独立的工具执行器组件 + + 可以直接输入聊天消息内容，自动判断并执行相应的工具，返回结构化的工具执行结果。 + """ + + def __init__(self, chat_id: str = None, enable_cache: bool = True, cache_ttl: int = 3): + """初始化工具执行器 + + Args: + executor_id: 执行器标识符，用于日志记录 + enable_cache: 是否启用缓存机制 + cache_ttl: 缓存生存时间（周期数） + """ + self.chat_id = chat_id + self.log_prefix = f"[ToolExecutor:{self.chat_id}] " + self.llm_model = LLMRequest( + model=global_config.model.tool_use, + request_type="tool_executor", + ) + + # 初始化工具实例 + self.tool_instance = ToolUser() + + # 缓存配置 + self.enable_cache = enable_cache + self.cache_ttl = cache_ttl + self.tool_cache = {} # 格式: {cache_key: {"result": result, "ttl": ttl, "timestamp": timestamp}} + + logger.info(f"{self.log_prefix}工具执行器初始化完成，缓存{'启用' if enable_cache else '禁用'}，TTL={cache_ttl}") + + async def execute_from_chat_message( + self, target_message: str, chat_history: list[str], sender: str, return_details: bool = False + ) -> List[Dict] | Tuple[List[Dict], List[str], str]: + """从聊天消息执行工具 + + Args: + target_message: 目标消息内容 + chat_history: 聊天历史 + sender: 发送者 + return_details: 是否返回详细信息(使用的工具列表和提示词) + + Returns: + 如果return_details为False: List[Dict] - 工具执行结果列表 + 如果return_details为True: Tuple[List[Dict], List[str], str] - (结果列表, 使用的工具, 提示词) + """ + + # 首先检查缓存 + cache_key = self._generate_cache_key(target_message, chat_history, sender) + cached_result = self._get_from_cache(cache_key) + + if cached_result: + logger.info(f"{self.log_prefix}使用缓存结果，跳过工具执行") + if return_details: + # 从缓存结果中提取工具名称 + used_tools = [result.get("tool_name", "unknown") for result in cached_result] + return cached_result, used_tools, "使用缓存结果" + else: + return cached_result + + # 缓存未命中，执行工具调用 + # 获取可用工具 + tools = self.tool_instance._define_tools() + + # 获取当前时间 + time_now = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()) + + bot_name = global_config.bot.nickname + + # 构建工具调用提示词 + prompt = await global_prompt_manager.format_prompt( + "tool_executor_prompt", + target_message=target_message, + chat_history=chat_history, + sender=sender, + bot_name=bot_name, + time_now=time_now, + ) + + logger.debug(f"{self.log_prefix}开始LLM工具调用分析") + + # 调用LLM进行工具决策 + response, other_info = await self.llm_model.generate_response_async(prompt=prompt, tools=tools) + + # 解析LLM响应 + if len(other_info) == 3: + reasoning_content, model_name, tool_calls = other_info + else: + reasoning_content, model_name = other_info + tool_calls = None + + # 执行工具调用 + tool_results, used_tools = await self._execute_tool_calls(tool_calls) + + # 缓存结果 + if tool_results: + self._set_cache(cache_key, tool_results) + + logger.info(f"{self.log_prefix}工具执行完成，共执行{len(used_tools)}个工具: {used_tools}") + + if return_details: + return tool_results, used_tools, prompt + else: + return tool_results + + async def _execute_tool_calls(self, tool_calls) -> Tuple[List[Dict], List[str]]: + """执行工具调用 + + Args: + tool_calls: LLM返回的工具调用列表 + + Returns: + Tuple[List[Dict], List[str]]: (工具执行结果列表, 使用的工具名称列表) + """ + tool_results = [] + used_tools = [] + + if not tool_calls: + logger.debug(f"{self.log_prefix}无需执行工具") + return tool_results, used_tools + + logger.info(f"{self.log_prefix}开始执行工具调用: {tool_calls}") + + # 处理工具调用 + success, valid_tool_calls, error_msg = process_llm_tool_calls(tool_calls) + + if not success: + logger.error(f"{self.log_prefix}工具调用解析失败: {error_msg}") + return tool_results, used_tools + + if not valid_tool_calls: + logger.debug(f"{self.log_prefix}无有效工具调用") + return tool_results, used_tools + + # 执行每个工具调用 + for tool_call in valid_tool_calls: + try: + tool_name = tool_call.get("name", "unknown_tool") + used_tools.append(tool_name) + + logger.debug(f"{self.log_prefix}执行工具: {tool_name}") + + # 执行工具 + result = await self.tool_instance._execute_tool_call(tool_call) + + if result: + tool_info = { + "type": result.get("type", "unknown_type"), + "id": result.get("id", f"tool_exec_{time.time()}"), + "content": result.get("content", ""), + "tool_name": tool_name, + "timestamp": time.time(), + } + tool_results.append(tool_info) + + logger.info(f"{self.log_prefix}工具{tool_name}执行成功，类型: {tool_info['type']}") + logger.debug(f"{self.log_prefix}工具{tool_name}结果内容: {tool_info['content'][:200]}...") + + except Exception as e: + logger.error(f"{self.log_prefix}工具{tool_name}执行失败: {e}") + # 添加错误信息到结果中 + error_info = { + "type": "tool_error", + "id": f"tool_error_{time.time()}", + "content": f"工具{tool_name}执行失败: {str(e)}", + "tool_name": tool_name, + "timestamp": time.time(), + } + tool_results.append(error_info) + + return tool_results, used_tools + + def _generate_cache_key(self, target_message: str, chat_history: list[str], sender: str) -> str: + """生成缓存键 + + Args: + target_message: 目标消息内容 + chat_history: 聊天历史 + sender: 发送者 + + Returns: + str: 缓存键 + """ + import hashlib + + # 使用消息内容和群聊状态生成唯一缓存键 + content = f"{target_message}_{chat_history}_{sender}" + return hashlib.md5(content.encode()).hexdigest() + + def _get_from_cache(self, cache_key: str) -> Optional[List[Dict]]: + """从缓存获取结果 + + Args: + cache_key: 缓存键 + + Returns: + Optional[List[Dict]]: 缓存的结果，如果不存在或过期则返回None + """ + if not self.enable_cache or cache_key not in self.tool_cache: + return None + + cache_item = self.tool_cache[cache_key] + if cache_item["ttl"] <= 0: + # 缓存过期，删除 + del self.tool_cache[cache_key] + logger.debug(f"{self.log_prefix}缓存过期，删除缓存键: {cache_key}") + return None + + # 减少TTL + cache_item["ttl"] -= 1 + logger.debug(f"{self.log_prefix}使用缓存结果，剩余TTL: {cache_item['ttl']}") + return cache_item["result"] + + def _set_cache(self, cache_key: str, result: List[Dict]): + """设置缓存 + + Args: + cache_key: 缓存键 + result: 要缓存的结果 + """ + if not self.enable_cache: + return + + self.tool_cache[cache_key] = {"result": result, "ttl": self.cache_ttl, "timestamp": time.time()} + logger.debug(f"{self.log_prefix}设置缓存，TTL: {self.cache_ttl}") + + def _cleanup_expired_cache(self): + """清理过期的缓存""" + if not self.enable_cache: + return + + expired_keys = [] + for cache_key, cache_item in self.tool_cache.items(): + if cache_item["ttl"] <= 0: + expired_keys.append(cache_key) + + for key in expired_keys: + del self.tool_cache[key] + + if expired_keys: + logger.debug(f"{self.log_prefix}清理了{len(expired_keys)}个过期缓存") + + def get_available_tools(self) -> List[str]: + """获取可用工具列表 + + Returns: + List[str]: 可用工具名称列表 + """ + tools = self.tool_instance._define_tools() + return [tool.get("function", {}).get("name", "unknown") for tool in tools] + + async def execute_specific_tool( + self, tool_name: str, tool_args: Dict, validate_args: bool = True + ) -> Optional[Dict]: + """直接执行指定工具 + + Args: + tool_name: 工具名称 + tool_args: 工具参数 + validate_args: 是否验证参数 + + Returns: + Optional[Dict]: 工具执行结果，失败时返回None + """ + try: + tool_call = {"name": tool_name, "arguments": tool_args} + + logger.info(f"{self.log_prefix}直接执行工具: {tool_name}") + + result = await self.tool_instance._execute_tool_call(tool_call) + + if result: + tool_info = { + "type": result.get("type", "unknown_type"), + "id": result.get("id", f"direct_tool_{time.time()}"), + "content": result.get("content", ""), + "tool_name": tool_name, + "timestamp": time.time(), + } + logger.info(f"{self.log_prefix}直接工具执行成功: {tool_name}") + return tool_info + + except Exception as e: + logger.error(f"{self.log_prefix}直接工具执行失败 {tool_name}: {e}") + + return None + + def clear_cache(self): + """清空所有缓存""" + if self.enable_cache: + cache_count = len(self.tool_cache) + self.tool_cache.clear() + logger.info(f"{self.log_prefix}清空了{cache_count}个缓存项") + + def get_cache_status(self) -> Dict: + """获取缓存状态信息 + + Returns: + Dict: 包含缓存统计信息的字典 + """ + if not self.enable_cache: + return {"enabled": False, "cache_count": 0} + + # 清理过期缓存 + self._cleanup_expired_cache() + + total_count = len(self.tool_cache) + ttl_distribution = {} + + for cache_item in self.tool_cache.values(): + ttl = cache_item["ttl"] + ttl_distribution[ttl] = ttl_distribution.get(ttl, 0) + 1 + + return { + "enabled": True, + "cache_count": total_count, + "cache_ttl": self.cache_ttl, + "ttl_distribution": ttl_distribution, + } + + def set_cache_config(self, enable_cache: bool = None, cache_ttl: int = None): + """动态修改缓存配置 + + Args: + enable_cache: 是否启用缓存 + cache_ttl: 缓存TTL + """ + if enable_cache is not None: + self.enable_cache = enable_cache + logger.info(f"{self.log_prefix}缓存状态修改为: {'启用' if enable_cache else '禁用'}") + + if cache_ttl is not None and cache_ttl > 0: + self.cache_ttl = cache_ttl + logger.info(f"{self.log_prefix}缓存TTL修改为: {cache_ttl}") + + +# 初始化提示词 +init_tool_executor_prompt() + + +""" +使用示例： + +# 1. 基础使用 - 从聊天消息执行工具（启用缓存，默认TTL=3） +executor = ToolExecutor(executor_id="my_executor") +results = await executor.execute_from_chat_message( + talking_message_str="今天天气怎么样？现在几点了？", + is_group_chat=False +) + +# 2. 禁用缓存的执行器 +no_cache_executor = ToolExecutor(executor_id="no_cache", enable_cache=False) + +# 3. 自定义缓存TTL +long_cache_executor = ToolExecutor(executor_id="long_cache", cache_ttl=10) + +# 4. 获取详细信息 +results, used_tools, prompt = await executor.execute_from_chat_message( + talking_message_str="帮我查询Python相关知识", + is_group_chat=False, + return_details=True +) + +# 5. 直接执行特定工具 +result = await executor.execute_specific_tool( + tool_name="get_knowledge", + tool_args={"query": "机器学习"} +) + +# 6. 缓存管理 +available_tools = executor.get_available_tools() +cache_status = executor.get_cache_status() # 查看缓存状态 +executor.clear_cache() # 清空缓存 +executor.set_cache_config(cache_ttl=5) # 动态修改缓存配置 +""" diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index c7ac59492..c4ddd21d8 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "2.28.0" +version = "3.2.0" #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读---- #如果你想要修改配置文件，请在修改后将version的值进行变更 @@ -44,7 +44,8 @@ compress_indentity = true # 是否压缩身份，压缩后会精简身份信息 [expression] # 表达方式 -expression_style = "描述麦麦说话的表达风格，表达习惯，例如：(回复尽量简短一些。可以参考贴吧，知乎和微博的回复风格，回复不要浮夸，不要用夸张修辞，平淡一些。不要有额外的符号，尽量简单简短)" +enable_expression = true # 是否启用表达方式 +expression_style = "描述麦麦说话的表达风格，表达习惯，例如：(请回复的平淡一些，简短一些，说中文，不要刻意突出自身学科背景。)" enable_expression_learning = false # 是否启用表达学习，麦麦会学习不同群里人类说话风格（群之间不互通） learning_interval = 600 # 学习间隔 单位秒 @@ -60,10 +61,14 @@ enable_relationship = true # 是否启用关系系统 relation_frequency = 1 # 关系频率，麦麦构建关系的速度，仅在normal_chat模式下有效 [chat] #麦麦的聊天通用设置 -chat_mode = "normal" # 聊天模式 —— 普通模式：normal，专注模式：focus，在普通模式和专注模式之间自动切换 +chat_mode = "normal" # 聊天模式 —— 普通模式：normal，专注模式：focus，自动auto：在普通模式和专注模式之间自动切换 # chat_mode = "focus" # chat_mode = "auto" +max_context_size = 18 # 上下文长度 + +replyer_random_probability = 0.5 # 首要replyer模型被选择的概率 + talk_frequency = 1 # 麦麦回复频率，越高，麦麦回复越频繁 time_based_talk_frequency = ["8:00,1", "12:00,1.5", "18:00,2", "01:00,0.5"] @@ -111,35 +116,29 @@ ban_msgs_regex = [ [normal_chat] #普通聊天 #一般回复参数 -normal_chat_first_probability = 0.5 # 麦麦回答时选择首要模型的概率（与之相对的，次要模型的概率为1 - normal_chat_first_probability） -max_context_size = 15 #上下文长度 -emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发 -thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢） +emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率 +thinking_timeout = 30 # 麦麦最长思考规划时间，超过这个时间的思考会放弃（往往是api反应太慢） willing_mode = "classical" # 回复意愿模式 —— 经典模式：classical，mxp模式：mxp，自定义模式：custom（需要你自己实现） response_interested_rate_amplifier = 1 # 麦麦回复兴趣度放大系数 -emoji_response_penalty = 0 # 对其他人发的表情包回复惩罚系数，设为0为不回复单个表情包，减少单独回复表情包的概率 mentioned_bot_inevitable_reply = true # 提及 bot 必然回复 at_bot_inevitable_reply = true # @bot 必然回复（包含提及） -enable_planner = false # 是否启用动作规划器（实验性功能，与focus_chat共享actions） +enable_planner = true # 是否启用动作规划器（与focus_chat共享actions） [focus_chat] #专注聊天 think_interval = 3 # 思考间隔 单位秒，可以有效减少消耗 consecutive_replies = 1 # 连续回复能力，值越高，麦麦连续回复的概率越高 -processor_max_time = 20 # 处理器最大时间，单位秒，如果超过这个时间，处理器会自动停止 -observation_context_size = 20 # 观察到的最长上下文大小 compressed_length = 8 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5 compress_length_limit = 4 #最多压缩份数，超过该数值的压缩上下文会被删除 - -[focus_chat_processor] # 专注聊天处理器，打开可以实现更多功能，但是会增加token消耗 -person_impression_processor = true # 是否启用关系识别处理器 -tool_use_processor = false # 是否启用工具使用处理器 working_memory_processor = false # 是否启用工作记忆处理器，消耗量大 -expression_selector_processor = true # 是否启用表达方式选择处理器 + +[tool] +enable_in_normal_chat = false # 是否在普通聊天中启用工具 +enable_in_focus_chat = true # 是否在专注聊天中启用工具 [emoji] max_reg_num = 60 # 表情包最大注册数量 @@ -168,7 +167,8 @@ consolidation_check_percentage = 0.05 # 检查节点比例 #不希望记忆的词，已经记忆的不会受到影响，需要手动清理 memory_ban_words = [ "表情包", "图片", "回复", "聊天记录" ] -[mood] # 仅在 普通聊天 有效 +[mood] # 暂时不再有效，请不要使用 +enable_mood = false # 是否启用情绪系统 mood_update_interval = 1.0 # 情绪更新间隔 单位秒 mood_decay_rate = 0.95 # 情绪衰减率 mood_intensity_factor = 1.0 # 情绪强度因子 @@ -241,7 +241,7 @@ library_log_levels = { "aiohttp" = "WARNING"} # 设置特定库的日志级别 # thinking_budget = : 用于指定模型思考最长长度 [model] -model_max_output_length = 800 # 模型单次返回的最大token数 +model_max_output_length = 1000 # 模型单次返回的最大token数 #------------必填：组件模型------------ @@ -270,12 +270,13 @@ pri_out = 8 #模型的输出价格（非必填，可以记录消耗） #默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数 temp = 0.2 #模型的温度，新V3建议0.1-0.3 -[model.replyer_2] # 一般聊天模式的次要回复模型 -name = "Pro/deepseek-ai/DeepSeek-R1" +[model.replyer_2] # 次要回复模型 +name = "Pro/deepseek-ai/DeepSeek-V3" provider = "SILICONFLOW" -pri_in = 4.0 #模型的输入价格（非必填，可以记录消耗） -pri_out = 16.0 #模型的输出价格（非必填，可以记录消耗） -temp = 0.7 +pri_in = 2 #模型的输入价格（非必填，可以记录消耗） +pri_out = 8 #模型的输出价格（非必填，可以记录消耗） +#默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数 +temp = 0.2 #模型的温度，新V3建议0.1-0.3 [model.memory_summary] # 记忆的概括模型 @@ -307,6 +308,13 @@ pri_out = 2.8 temp = 0.7 enable_thinking = false # 是否启用思考 +[model.tool_use] #工具调用模型，需要使用支持工具调用的模型 +name = "Qwen/Qwen3-14B" +provider = "SILICONFLOW" +pri_in = 0.5 +pri_out = 2 +temp = 0.7 +enable_thinking = false # 是否启用思考（qwen3 only） #嵌入模型 [model.embedding] @@ -326,15 +334,6 @@ pri_out = 2.8 temp = 0.7 -[model.focus_tool_use] #工具调用模型，需要使用支持工具调用的模型 -name = "Qwen/Qwen3-14B" -provider = "SILICONFLOW" -pri_in = 0.5 -pri_out = 2 -temp = 0.7 -enable_thinking = false # 是否启用思考（qwen3 only） - - #------------LPMM知识库模型------------ [model.lpmm_entity_extract] # 实体提取模型